diff --git a/.github/workflows/conda.yaml b/.github/workflows/conda.yaml
index fe1da71aa..d4461b28a 100644
--- a/.github/workflows/conda.yaml
+++ b/.github/workflows/conda.yaml
@@ -22,27 +22,26 @@ jobs:
       matrix:
         include:
           - os: ubuntu-latest
-            python: "3.12"
+            python: "3.11"
 
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python }}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - name: Setup Miniconda
-        uses: conda-incubator/setup-miniconda@v2
+        uses: conda-incubator/setup-miniconda@v3
         with:
-          miniforge-variant: Mambaforge
-          miniforge-version: latest
+          mamba-version: "*"
           channels: conda-forge,bioconda
           channel-priority: strict
-          python-version: ${{ matrix.python-version }}
+          python-version: ${{ matrix.python }}
 
       - name: install conda build
         run: |
-          mamba install -y boa conda-verify
+          mamba install -y boa conda-verify python=${{ matrix.python }}
         shell: bash
 
       - name: build and test package
diff --git a/CHANGELOG.md b/CHANGELOG.md
index c29438c55..cd305992a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,20 +8,22 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
-## [Unreleased]
+## v0.18.0
 
 ### Additions
 
 -   Isotypically included B cells are now labelled as `receptor_subtype="IGH+IGK/L"` instead of `ambiguous` in `tl.chain_qc` ([#537](https://github.com/scverse/scirpy/pull/537)).
 -   Added the `normalized_hamming` metric to `pp.ir_dist` that accounts for differences in CDR3 sequence length ([#512](https://github.com/scverse/scirpy/pull/512)).
+-   `tl.define_clonotype_clusters` now has an option to require J genes to match (`same_j_gene=True`) in addition to `same_v_gene`. ([#470](https://github.com/scverse/scirpy/pull/470)).
 
 ### Performance improvements
 
--   The hamming distance was reimplemented with numba, achieving a significant speedup ([#512](https://github.com/scverse/scirpy/pull/512)).
+-   The hamming distance has been reimplemented with numba, achieving a significant speedup ([#512](https://github.com/scverse/scirpy/pull/512)).
+-   Clonotype clustering has been accelerated leveraging sparse matrix operations ([#470](https://github.com/scverse/scirpy/pull/470)).
 
 ### Fixes
 
--   Fix that pl.clonotype_network couldn't use non-standard obsm key ([#545](https://github.com/scverse/scirpy/pull/545)).
+-   Fix that `pl.clonotype_network` couldn't use non-standard obsm key ([#545](https://github.com/scverse/scirpy/pull/545)).
 
 ### Other changes
 
@@ -54,7 +56,7 @@ and this project adheres to [Semantic Versioning][].
 
 ### Fixes
 
--   Fix issue with detecting the number of available CPUs on MacOD ([#518](https://github.com/scverse/scirpy/pull/502))
+-   Fix issue with detecting the number of available CPUs on MacOS ([#518](https://github.com/scverse/scirpy/pull/502))
 
 ## v0.16.1
 
diff --git a/docs/tutorials/tutorial_3k_tcr.ipynb b/docs/tutorials/tutorial_3k_tcr.ipynb
index 84c94e9ac..f15285067 100644
--- a/docs/tutorials/tutorial_3k_tcr.ipynb
+++ b/docs/tutorials/tutorial_3k_tcr.ipynb
@@ -880,30 +880,10 @@
       "Computing sequence x sequence distance matrix for VJ sequences.\n",
       "Computing sequence x sequence distance matrix for VDJ sequences.\n",
       "Initializing lookup tables. \n",
-      "Computing clonotype x clonotype distances.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1526/1526 [00:00<00:00, 1526.57it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Computing clonotype x clonotype distances.\n",
       "Stored result in `mdata.obs[\"airr:clone_id\"]`.\n",
       "Stored result in `mdata.obs[\"airr:clone_id_size\"]`.\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
     }
    ],
    "source": [
@@ -1069,30 +1049,10 @@
      "output_type": "stream",
      "text": [
       "Initializing lookup tables. \n",
-      "Computing clonotype x clonotype distances.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1549/1549 [00:02<00:00, 570.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Computing clonotype x clonotype distances.\n",
       "Stored result in `mdata.obs[\"airr:cc_aa_tcrdist\"]`.\n",
       "Stored result in `mdata.obs[\"airr:cc_aa_tcrdist_size\"]`.\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
     }
    ],
    "source": [
@@ -1332,30 +1292,10 @@
      "output_type": "stream",
      "text": [
       "Initializing lookup tables. \n",
-      "Computing clonotype x clonotype distances.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1549/1549 [00:03<00:00, 508.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Computing clonotype x clonotype distances.\n",
       "Stored result in `mdata.obs[\"airr:cc_aa_tcrdist_same_v\"]`.\n",
       "Stored result in `mdata.obs[\"airr:cc_aa_tcrdist_same_v_size\"]`.\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
     }
    ],
    "source": [
@@ -2697,7 +2637,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1490/1490 [00:00<00:00, 79003.75it/s]"
+      "100%|██████████| 1490/1490 [00:00<00:00, 84735.71it/s]"
      ]
     },
     {
@@ -2712,7 +2652,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "100%|██████████| 1000/1000 [00:00<00:00, 2022.26it/s]\n"
+      "100%|██████████| 1000/1000 [00:00<00:00, 2187.13it/s]\n"
      ]
     },
     {
@@ -2823,7 +2763,7 @@
     },
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 680x320 with 3 Axes>"
       ]
@@ -2926,7 +2866,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ranking genes\n",
+      "ranking genes\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    finished (0:00:00)\n"
      ]
     },
@@ -2990,9 +2936,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/sturm/projects/2020/scirpy/src/scirpy/tl/_clonotype_imbalance.py:273: RuntimeWarning: divide by zero encountered in log2\n",
+      "/home/sturm/projects/2020/scirpy/src/scirpy/tl/_clonotype_imbalance.py:272: RuntimeWarning: divide by zero encountered in log2\n",
       "  logfoldchange = np.log2((case_mean_freq + global_minimum) / (control_mean_freq + global_minimum))\n",
-      "/home/sturm/projects/2020/scirpy/src/scirpy/tl/_clonotype_imbalance.py:273: RuntimeWarning: divide by zero encountered in scalar divide\n",
+      "/home/sturm/projects/2020/scirpy/src/scirpy/tl/_clonotype_imbalance.py:272: RuntimeWarning: divide by zero encountered in scalar divide\n",
       "  logfoldchange = np.log2((case_mean_freq + global_minimum) / (control_mean_freq + global_minimum))\n"
      ]
     }
@@ -3254,29 +3200,9 @@
      "output_type": "stream",
      "text": [
       "Initializing lookup tables. \n",
-      "Computing clonotype x clonotype distances.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1549/1549 [00:01<00:00, 839.84it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Computing clonotype x clonotype distances.\n",
       "Stored IR distance matrix in `adata.uns[\"ir_query_VDJDB_aa_identity\"]`.\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
     }
    ],
    "source": [
@@ -3412,7 +3338,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 324/324 [00:00<00:00, 8583.86it/s]"
+      "100%|██████████| 324/324 [00:00<00:00, 8135.75it/s]"
      ]
     },
     {
diff --git a/src/scirpy/ir_dist/_clonotype_neighbors.py b/src/scirpy/ir_dist/_clonotype_neighbors.py
index e2171b755..c948c617e 100644
--- a/src/scirpy/ir_dist/_clonotype_neighbors.py
+++ b/src/scirpy/ir_dist/_clonotype_neighbors.py
@@ -6,13 +6,12 @@
 import pandas as pd
 import scipy.sparse as sp
 from scanpy import logging
-from tqdm.contrib.concurrent import process_map
 
 from scirpy.get import _has_ir
 from scirpy.get import airr as get_airr
-from scirpy.util import DataHandler, _get_usable_cpus, tqdm
+from scirpy.util import DataHandler
 
-from ._util import DoubleLookupNeighborFinder, merge_coo_matrices, reduce_and, reduce_or
+from ._util import DoubleLookupNeighborFinder
 
 
 class ClonotypeNeighbors:
@@ -24,6 +23,7 @@ def __init__(
         receptor_arms: Literal["VJ", "VDJ", "all", "any"],
         dual_ir: Literal["primary_only", "all", "any"],
         same_v_gene: bool = False,
+        same_j_gene: bool = False,
         match_columns: None | Sequence[str] = None,
         distance_key: str,
         sequence_key: str,
@@ -34,6 +34,7 @@ def __init__(
         receptor configuration and calls clonotypes from this distance matrix
         """
         self.same_v_gene = same_v_gene
+        self.same_j_gene = same_j_gene
         self.match_columns = match_columns
         self.receptor_arms = receptor_arms
         self.dual_ir = dual_ir
@@ -67,8 +68,13 @@ def _make_clonotype_table(self, params: DataHandler) -> tuple[Mapping, pd.DataFr
             raise ValueError("Obs names need to be unique!")
 
         airr_variables = [self.sequence_key]
+
         if self.same_v_gene:
             airr_variables.append("v_call")
+
+        if self.same_j_gene:
+            airr_variables.append("j_call")
+
         chains = [f"{arm}_{chain}" for arm, chain in itertools.product(self._receptor_arm_cols, self._dual_ir_cols)]
 
         obs = get_airr(params, airr_variables, chains)
@@ -157,13 +163,28 @@ def _add_distance_matrices(self) -> None:
                     self.clonotypes2,
                     [x for x in self.clonotypes.columns if "v_call" in x],
                 )
-
             self.neighbor_finder.add_distance_matrix(
                 "v_gene",
                 sp.identity(len(v_genes), dtype=bool, format="csr"),
                 v_genes,  # type: ignore
             )
 
+        if self.same_j_gene:
+            # J gene distance matrix (identity mat)
+            j_genes = self._unique_values_in_multiple_columns(
+                self.clonotypes, [x for x in self.clonotypes.columns if "j_call" in x]
+            )
+            if self.clonotypes2 is not None:
+                j_genes |= self._unique_values_in_multiple_columns(
+                    self.clonotypes2,
+                    [x for x in self.clonotypes.columns if "j_call" in x],
+                )
+            self.neighbor_finder.add_distance_matrix(
+                "j_gene",
+                sp.identity(len(j_genes), dtype=bool, format="csr"),
+                j_genes,  # type: ignore
+            )
+
         if self.match_columns is not None:
             match_columns_values = set(self.clonotypes["match_columns"].values)
             if self.clonotypes2 is not None:
@@ -190,6 +211,13 @@ def _add_lookup_tables(self) -> None:
                     "v_gene",
                     dist_type="boolean",
                 )
+            if self.same_j_gene:
+                self.neighbor_finder.add_lookup_table(
+                    f"{arm}_{i}_j_call",
+                    f"{arm}_{i}_j_call",
+                    "j_gene",
+                    dist_type="boolean",
+                )
 
         if self.match_columns is not None:
             self.neighbor_finder.add_lookup_table(
@@ -208,34 +236,17 @@ def compute_distances(self) -> sp.csr_matrix:
         """
         start = logging.info("Computing clonotype x clonotype distances.")  # type: ignore
         n_clonotypes = self.clonotypes.shape[0]
+        clonotype_ids = np.arange(n_clonotypes)
 
-        # only use multiprocessing for sufficiently large datasets
-        # for small datasets the overhead is too large for a benefit
-        if self.n_jobs == 1 or n_clonotypes <= 2 * self.chunksize:
-            dist_rows = tqdm(
-                (self._dist_for_clonotype(i) for i in range(n_clonotypes)),
-                total=n_clonotypes,
-            )
-        else:
-            logging.info(
-                "NB: Computation happens in chunks. The progressbar only advances " "when a chunk has finished. "
-            )  # type: ignore
-
-            dist_rows = process_map(
-                self._dist_for_clonotype,
-                range(n_clonotypes),
-                max_workers=_get_usable_cpus(self.n_jobs),
-                chunksize=2000,
-                tqdm_class=tqdm,
-            )
+        dist = self._dist_for_clonotype(clonotype_ids)
 
-        dist = sp.vstack(list(dist_rows))
         dist.eliminate_zeros()
         logging.hint("Done computing clonotype x clonotype distances. ", time=start)
         return dist  # type: ignore
 
-    def _dist_for_clonotype(self, ct_id: int) -> sp.csr_matrix:
-        """Compute neighboring clonotypes for a given clonotype.
+    def _dist_for_clonotype(self, ct_ids: np.ndarray[int]) -> sp.csr_matrix:
+        """Compute neighboring clonotypes for the given clonotypes.
+        Returns a clonotype x clonotype2 sparse distance matrix.
 
         Or operations use the min dist of two matching entries.
         And operations use the max dist of two matching entries.
@@ -245,83 +256,249 @@ def _dist_for_clonotype(self, ct_id: int) -> sp.csr_matrix:
         has a sequence dist < threshold. If we require both receptors to
         match ("and"), the higher one should count.
         """
-        # Lookup distances for current row
-        tmp_clonotypes = self.clonotypes2 if self.clonotypes2 is not None else self.clonotypes
-        lookup = {}  # CDR3 distances
-        lookup_v = {}  # V-gene distances
-        for tmp_arm in self._receptor_arm_cols:
-            chain_ids = [(1, 1)] if self.dual_ir == "primary_only" else [(1, 1), (2, 2), (1, 2), (2, 1)]
+        lookup = {}
+        chain_ids = [(1, 1)] if self.dual_ir == "primary_only" else [(1, 1), (2, 2), (1, 2), (2, 1)]
+        for receptor_arm in self._receptor_arm_cols:
             for c1, c2 in chain_ids:
-                lookup[(tmp_arm, c1, c2)] = self.neighbor_finder.lookup(
-                    ct_id,
-                    f"{tmp_arm}_{c1}",
-                    f"{tmp_arm}_{c2}",
+                lookup[(receptor_arm, c1, c2)] = self.neighbor_finder.lookup(
+                    ct_ids,
+                    f"{receptor_arm}_{c1}",
+                    f"{receptor_arm}_{c2}",
                 )
-                if self.same_v_gene:
-                    lookup_v[(tmp_arm, c1, c2)] = self.neighbor_finder.lookup(
-                        ct_id,
-                        f"{tmp_arm}_{c1}_v_call",
-                        f"{tmp_arm}_{c2}_v_call",
-                    )
-
-        # need to loop through all coordinates that have at least one distance.
-        has_distance = merge_coo_matrices(lookup.values()).tocsr()  # type: ignore
-        # convert to csr matrices to iterate over indices
-        lookup = {k: v.tocsr() for k, v in lookup.items()}
-
-        def _lookup_dist_for_chains(tmp_arm: Literal["VJ", "VDJ"], c1: Literal[1, 2], c2: Literal[1, 2]):
-            """Lookup the distance between two chains of a given receptor
-            arm. Only considers those columns in the current row that
-            have an entry in `has_distance`. Returns a dense
-            array with dimensions (1, n) where n equals the number
-            of entries in `has_distance`.
+        id_len = len(ct_ids)
+
+        first_value = next(iter(lookup.values()))
+        has_distance_table = sp.csr_matrix((id_len, first_value.shape[1]))
+        for value in lookup.values():
+            has_distance_table += value
+
+        has_distance_mask = has_distance_table
+        has_distance_mask.data = np.ones_like(has_distance_mask.data)
+
+        def OR_min(a: sp.csr_matrix, b: sp.csr_matrix) -> sp.csr_matrix:
             """
-            ct_col2 = tmp_clonotypes[f"{tmp_arm}_{c2}_{self.sequence_key}"].values
-            tmp_array = lookup[(tmp_arm, c1, c2)][0, has_distance.indices].todense().A1.astype(np.float16)
-            tmp_array[ct_col2[has_distance.indices] == "nan"] = np.nan
-            if self.same_v_gene:
-                mask_v_gene = lookup_v[(tmp_arm, c1, c2)][0, has_distance.indices]
-                tmp_array = np.multiply(tmp_array, mask_v_gene)
-            return tmp_array
+            Computes the element-wise minimum between 2 CSR matrices while ignoring 0 values. If 2 values
+            are compared and at least one of them is a 0, the maximum of the 2 values is taken instead of the minimum.
+
+            To be able to use built-in functions, we shift the data arrays by the overall maximum value such that we get negative values.
+            Then we can use the built-in "<" function to compare the CSR matrices while ignoring 0 values.
+            """
+            max_value_a = np.max(a.data, initial=0)
+            max_value_b = np.max(b.data, initial=0)
+
+            if max_value_a > np.iinfo(np.uint8).max or max_value_b > np.iinfo(np.uint8).max:
+                raise ValueError("CSR matrix data values exceed maximum value for datatype uint8 (255).")
+
+            max_value = np.int16(np.max([max_value_a, max_value_b]) + 1)
+            min_mat_a = sp.csr_matrix((a.data.astype(np.int16), a.indices, a.indptr), shape=a.shape)
+            min_mat_a.data -= max_value
+            min_mat_b = sp.csr_matrix((b.data.astype(np.int16), b.indices, b.indptr), shape=b.shape)
+            min_mat_b.data -= max_value
+            a_smaller_b = min_mat_a < min_mat_b
+            min_result = b + (a - b).multiply(a_smaller_b)
+            return min_result
+
+        def AND_max(a, b):
+            """
+            Computes the element-wise maximum between 2 CSR matrices while handling 0 values differently. If 2 values
+            are compared and at least one of them is a 0, the minimum (=0) of the 2 values is taken instead of the maximum.
+
+            To be able to use built-in functions, we shift the data arrays by the overall maximum value such that we get negative values.
+            Then we can use the built-in ">" function to compare the CSR matrices while handling the special case for 0 values at
+            the same time.
+            """
+            max_value_a = np.max(a.data, initial=0)
+            max_value_b = np.max(b.data, initial=0)
+
+            if max_value_a > np.iinfo(np.uint8).max or max_value_b > np.iinfo(np.uint8).max:
+                raise ValueError("CSR matrix data values exceed maximum value for datatype uint8 (255).")
+
+            max_value = np.int16(np.max([max_value_a, max_value_b]) + 1)
+            max_mat_a = sp.csr_matrix((a.data.astype(np.int16), a.indices, a.indptr), shape=a.shape)
+            max_mat_a.data -= max_value
+            max_mat_b = sp.csr_matrix((b.data.astype(np.int16), b.indices, b.indptr), shape=b.shape)
+            max_mat_b.data -= max_value
+            a_greater_b = max_mat_a > max_mat_b
+            max_result = b + (a - b).multiply(a_greater_b)
+            return max_result
+
+        if self.match_columns is not None:
+            # Create a mask to filter clonotype pairs based on having similar entries in given columns
+            distance_matrix_name, forward, _ = self.neighbor_finder.lookups["match_columns"]
+            distance_matrix_name_reverse, _, reverse = self.neighbor_finder.lookups["match_columns"]
+            if distance_matrix_name != distance_matrix_name_reverse:
+                raise ValueError("Forward and reverse lookup tablese must be defined " "on the same distance matrices.")
+            reverse_lookup_values = np.vstack(list(reverse.lookup.values()))
+            reverse_lookup_keys = np.zeros(reverse.size, dtype=np.int64)
+            reverse_lookup_keys[list(reverse.lookup.keys())] = np.arange(len(list(reverse.lookup.keys())))
+            match_column_mask = sp.csr_matrix(
+                (np.empty(len(has_distance_mask.indices)), has_distance_mask.indices, has_distance_mask.indptr),
+                shape=has_distance_mask.shape,
+            )
+            has_distance_mask_coo = match_column_mask.tocoo()
+            indices_in_dist_mat = forward[has_distance_mask_coo.row]
+            match_column_mask.data = reverse_lookup_values[
+                reverse_lookup_keys[indices_in_dist_mat], has_distance_mask_coo.col
+            ]
+
+        receptor_arm_res = {}
+        dist_mats_chains = {}
+
+        def filter_chain_count_data(
+            dist_mat_coo,
+            chain_counts_a,
+            chain_counts_b,
+        ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+            """
+            Helper function for filter_chain_count. Computes the data
+            arrays for the csr matrices that we want to filter by chain count.
+            """
+            filtered_data_stacked = np.array(
+                [np.zeros_like(dist_mat_coo.data), np.zeros_like(dist_mat_coo.data), np.zeros_like(dist_mat_coo.data)]
+            )
+
+            ct_pair_ids_a = dist_mat_coo.row
+            ct_pair_ids_b = dist_mat_coo.col
+
+            data_array_indices = np.arange(len(dist_mat_coo.data))
+            chain_counts_pair_a = chain_counts_a[ct_pair_ids_a]
+            chain_counts_pair_b = chain_counts_b[ct_pair_ids_b]
+            chain_counts_equal = chain_counts_pair_a == chain_counts_pair_b
+
+            filtered_data_stacked[chain_counts_pair_a[chain_counts_equal], data_array_indices[chain_counts_equal]] = (
+                dist_mat_coo.data[chain_counts_equal]
+            )
+
+            return (
+                filtered_data_stacked[0],
+                filtered_data_stacked[1],
+                filtered_data_stacked[2],
+            )
+
+        def filter_chain_count(
+            tmp_dist_mat: sp.csr_matrix, col: str
+        ) -> tuple[sp.csr_matrix, sp.csr_matrix, sp.csr_matrix]:
+            """
+            Filters a temporary clonotype distance matrix based on the count of receptor chains.
+            We want to keep clonotype pairs that both have the same number of chains which can be either 0, 1, or 2 chains.
+            Return 3 matrices: pairs with both 0 chains, pairs with both 1 chain, pairs with both 2 chains;
+            """
+            chain_counts_a = self._chain_count[col]
+
+            if self._chain_count2 is None:
+                chain_counts_b = chain_counts_a
+            else:
+                chain_counts_b = self._chain_count2[col]
+
+            dist_mat_coo = tmp_dist_mat.tocoo()
+
+            filtered_chain_count0, filtered_chain_count1, filtered_chain_count2 = (
+                tmp_dist_mat.copy(),
+                tmp_dist_mat.copy(),
+                tmp_dist_mat.copy(),
+            )
+            filtered_chain_count0.data, filtered_chain_count1.data, filtered_chain_count2.data = (
+                filter_chain_count_data(
+                    dist_mat_coo,
+                    chain_counts_a,
+                    chain_counts_b,
+                )
+            )
+            return filtered_chain_count0, filtered_chain_count1, filtered_chain_count2
+
+        def match_gene_segment(
+            tmp_dist_mat: sp.csr_matrix, tmp_arm: str, c1: int, c2: int, segment_suffix: Literal["v_call", "j_call"]
+        ) -> sp.csr_matrix:
+            """
+            Filters a temporary clonotype distance matrix based on gene segement similarity (e.g. v_gene or j_gene).
+            We want to keep clonotype pairs with the same gene segment.
+            """
+            distance_matrix_name, forward, _ = self.neighbor_finder.lookups[f"{tmp_arm}_{c1}_{segment_suffix}"]
+            distance_matrix_name_reverse, _, reverse = self.neighbor_finder.lookups[f"{tmp_arm}_{c2}_{segment_suffix}"]
+            if distance_matrix_name != distance_matrix_name_reverse:
+                raise ValueError("Forward and reverse lookup tablese must be defined " "on the same distance matrices.")
+            empty_row = np.array([np.zeros(reverse.size, dtype=bool)])
+            reverse_lookup_values = np.vstack((*reverse.lookup.values(), empty_row))
+            reverse_lookup_keys = np.full(id_len, -1, dtype=np.int32)
+            keys_array = np.fromiter(reverse.lookup.keys(), dtype=int, count=len(reverse.lookup))
+            reverse_lookup_keys[keys_array] = np.arange(len(keys_array))
+            gene_segment_mask = sp.csr_matrix(
+                (np.empty(len(has_distance_mask.indices)), has_distance_mask.indices, has_distance_mask.indptr),
+                shape=has_distance_mask.shape,
+            )
+            has_distance_mask_coo = gene_segment_mask.tocoo()
+            indices_in_dist_mat = forward[has_distance_mask_coo.row]
+            gene_segment_mask.data = reverse_lookup_values[
+                reverse_lookup_keys[indices_in_dist_mat], has_distance_mask_coo.col
+            ]
+            return tmp_dist_mat.multiply(gene_segment_mask)
+
+        # Now we merge the distances of chains.
+        # Can first be filtered based on v_gene and j_gene similarity and other column similarity.
+        # We also need to filter based on chain count similarity.
+        # Then we reduce the temporary clonotype distance matrices of the receptor chains with an AND/OR logic.
+        for receptor_arm in self._receptor_arm_cols:
+            for c1, c2 in chain_ids:
+                tmp_dist_mat = lookup[(receptor_arm, c1, c2)][ct_ids]
+
+                if not (self.same_v_gene or self.same_j_gene or self.match_columns):
+                    tmp_dist_mat = tmp_dist_mat.multiply(has_distance_mask)
+
+                if self.same_v_gene:
+                    tmp_dist_mat = match_gene_segment(tmp_dist_mat, receptor_arm, c1, c2, segment_suffix="v_call")
+
+                if self.same_j_gene:
+                    tmp_dist_mat = match_gene_segment(tmp_dist_mat, receptor_arm, c1, c2, segment_suffix="j_call")
+
+                if self.match_columns is not None:
+                    tmp_dist_mat = tmp_dist_mat.multiply(match_column_mask)
+
+                if self.dual_ir == "all":
+                    filtered0, filtered1, filtered2 = filter_chain_count(tmp_dist_mat, receptor_arm)
+                    dist_mats_chains[(receptor_arm, c1, c2, 0)] = filtered0
+                    dist_mats_chains[(receptor_arm, c1, c2, 1)] = filtered1
+                    dist_mats_chains[(receptor_arm, c1, c2, 2)] = filtered2
+                else:
+                    dist_mats_chains[(receptor_arm, c1, c2)] = tmp_dist_mat
 
-        # Merge the distances of chains
-        res = []
-        for tmp_arm in self._receptor_arm_cols:
             if self.dual_ir == "primary_only":
-                tmp_res = _lookup_dist_for_chains(tmp_arm, 1, 1)
-            elif self.dual_ir == "all":
-                tmp_res = reduce_or(
-                    reduce_and(
-                        _lookup_dist_for_chains(tmp_arm, 1, 1),
-                        _lookup_dist_for_chains(tmp_arm, 2, 2),
-                        chain_count=self._chain_count[tmp_arm][ct_id],
-                    ),
-                    reduce_and(
-                        _lookup_dist_for_chains(tmp_arm, 1, 2),
-                        _lookup_dist_for_chains(tmp_arm, 2, 1),
-                        chain_count=self._chain_count[tmp_arm][ct_id],
-                    ),
+                receptor_arm_res[receptor_arm] = dist_mats_chains[(receptor_arm, 1, 1)]
+            elif self.dual_ir == "any":
+                receptor_arm_res[receptor_arm] = OR_min(
+                    OR_min(dist_mats_chains[(receptor_arm, 1, 1)], dist_mats_chains[(receptor_arm, 1, 2)]),
+                    OR_min(dist_mats_chains[(receptor_arm, 2, 1)], dist_mats_chains[(receptor_arm, 2, 2)]),
                 )
-            else:  # "any"
-                tmp_res = reduce_or(
-                    _lookup_dist_for_chains(tmp_arm, 1, 1),
-                    _lookup_dist_for_chains(tmp_arm, 1, 2),
-                    _lookup_dist_for_chains(tmp_arm, 2, 2),
-                    _lookup_dist_for_chains(tmp_arm, 2, 1),
+            elif self.dual_ir == "all":
+                receptor_arm_res[receptor_arm] = OR_min(
+                    AND_max(dist_mats_chains[(receptor_arm, 1, 1, 2)], dist_mats_chains[(receptor_arm, 2, 2, 2)]),
+                    AND_max(dist_mats_chains[(receptor_arm, 2, 1, 2)], dist_mats_chains[(receptor_arm, 1, 2, 2)]),
                 )
 
-            res.append(tmp_res)
-
-        # Merge the distances of arms.
-        reduce_fun = reduce_and if self.receptor_arms == "all" else reduce_or
-        # checking only the chain=1 columns here is enough, as there must not
-        # be a secondary chain if there is no first one.
-        res = reduce_fun(np.vstack(res), chain_count=self._chain_count["arms"][ct_id])
+                receptor_arm_res[receptor_arm] += (
+                    dist_mats_chains[(receptor_arm, 1, 1, 1)] + dist_mats_chains[(receptor_arm, 1, 1, 0)]
+                )
+            else:
+                raise NotImplementedError(f"self.dual_ir method {self.dual_ir} is not implemented")
 
-        if self.match_columns is not None:
-            match_columns_mask = self.neighbor_finder.lookup(ct_id, "match_columns", "match_columns")
-            res = np.multiply(res, match_columns_mask[0, has_distance.indices])
+        if len(receptor_arm_res) == 1:
+            ct_dist_mat = receptor_arm_res[self._receptor_arm_cols[0]]
+        else:
+            if self.receptor_arms == "all":
+                arm_res_filtered = {}
+                arm_res_filtered[("VJ", 0)], arm_res_filtered[("VJ", 1)], arm_res_filtered[("VJ", 2)] = (
+                    filter_chain_count(receptor_arm_res["VJ"], "arms")
+                )
+                arm_res_filtered[("VDJ", 0)], arm_res_filtered[("VDJ", 1)], arm_res_filtered[("VDJ", 2)] = (
+                    filter_chain_count(receptor_arm_res["VDJ"], "arms")
+                )
+                ct_dist_mat = AND_max(arm_res_filtered[("VJ", 2)], arm_res_filtered[("VDJ", 2)])
+                ct_dist_mat += (
+                    arm_res_filtered[("VJ", 0)]
+                    + arm_res_filtered[("VJ", 1)]
+                    + arm_res_filtered[("VDJ", 0)]
+                    + arm_res_filtered[("VDJ", 1)]
+                )
 
-        final_res = has_distance.copy()
-        final_res.data = res.astype(np.uint8)
-        return final_res
+            else:
+                ct_dist_mat = OR_min(receptor_arm_res["VJ"], receptor_arm_res["VDJ"])
+        return ct_dist_mat
diff --git a/src/scirpy/ir_dist/_util.py b/src/scirpy/ir_dist/_util.py
index b0c3bbeb2..08ffa9228 100644
--- a/src/scirpy/ir_dist/_util.py
+++ b/src/scirpy/ir_dist/_util.py
@@ -233,61 +233,90 @@ def n_cols(self):
 
     def lookup(
         self,
-        object_id: int,
-        forward_lookup_table: str,
-        reverse_lookup_table: str | None = None,
-    ) -> coo_matrix | np.ndarray:
-        """Get ids of neighboring objects from a lookup table.
-
-        Performs the following lookup:
+        object_ids: np.ndarray[int],
+        forward_lookup_table_name: str,
+        reverse_lookup_table_name: str | None = None,
+    ) -> sp.csr_matrix:
+        """
+        Creates a distance matrix between objects with the given ids based on a feature distance matrix.
 
-            object_id -> dist_mat -> neighboring features -> neighboring objects.
+        To get the distance between two objects we need to look up the features of the two objects.
+        The distance between those two features is then the distance between the two objects.
 
-        where an object is a clonotype in our case (but could be used for something else)
+        To do so, we first use the `object_ids` together with the `forward_lookup_table` to look up
+        the indices of the objects in the feature `distance_matrix`. Afterwards we pick the according row for each object
+        out of the `distance_matrix` and construct a `rows` matrix (n_object_ids x n_features).
 
-        "nan"s are not looked up via the distance matrix, they return a row of zeros
+        "nan"s (index = -1) are not looked up in the feature `distance_matrix`, they return a row of zeros
         instead.
 
+        Then we use the entries of the `reverse_lookup_table` to construct a `reverse_lookup_matrix` (n_features x n_object_ids).
+        By multiplying the `rows` matrix with the `reverse_lookup_matrix` we get the final `object_distance_matrix` that shows
+        the distances between the objects with the given `object_ids` regarding a certain feature column.
+
+        It might not be obvious at the first sight that the matrix multiplication between `rows` and `reverse_lookup_matrix` gives
+        us the desired result. But this trick allows us to use the built-in sparse matrix multiplication of `scipy.sparse`
+        for enhanced performance.
+
         Parameters
         ----------
-        object_id
-            The row index of the feature_table.
-        forward_lookup_table
+        object_ids
+            The row indices of the feature_table.
+        forward_lookup_table_name
             The unique identifier of a lookup table previously added via
             `add_lookup_table`.
-        reverse_lookup_table
+        reverse_lookup_table_name
             The unique identifier of the lookup table used for the reverse lookup.
             If not provided will use the same lookup table for forward and reverse
             lookup. This is useful to calculate distances across features from
             different columns of the feature table (e.g. primary and secondary VJ chains).
+
+        Returns
+        -------
+        object_distance_matrix
+            A CSR matrix containing the pairwise distances between objects with the
+            given `object_ids` regarding a certain feature column.
         """
-        distance_matrix_name, forward, reverse = self.lookups[forward_lookup_table]
+        distance_matrix_name, forward_lookup_table, reverse_lookup_table = self.lookups[forward_lookup_table_name]
 
-        if reverse_lookup_table is not None:
-            distance_matrix_name_reverse, _, reverse = self.lookups[reverse_lookup_table]
+        if reverse_lookup_table_name is not None:
+            distance_matrix_name_reverse, _, reverse_lookup_table = self.lookups[reverse_lookup_table_name]
             if distance_matrix_name != distance_matrix_name_reverse:
                 raise ValueError("Forward and reverse lookup tablese must be defined " "on the same distance matrices.")
 
         distance_matrix = self.distance_matrices[distance_matrix_name]
-        idx_in_dist_mat = forward[object_id]
-        if idx_in_dist_mat == -1:  # nan
-            return reverse.empty()
-        else:
-            # get distances from the distance matrix...
-            row = distance_matrix[idx_in_dist_mat, :]
-
-            if reverse.is_boolean:
-                assert (
-                    len(row.indices) == 1  # type: ignore
-                ), "Boolean reverse lookup only works for identity distance matrices."
-                return reverse[row.indices[0]]  # type: ignore
-            else:
-                # ... and get column indices directly from sparse row
-                # sum concatenates coo matrices
-                return merge_coo_matrices(
-                    (reverse[i] * multiplier for i, multiplier in zip(row.indices, row.data, strict=False)),  # type: ignore
-                    shape=(1, reverse.size),
-                )
+
+        if np.max(distance_matrix.data) > np.iinfo(np.uint8).max:
+            raise OverflowError(
+                "The data values in the distance scipy.sparse.csr_matrix exceed the maximum value for uint8 (255)"
+            )
+
+        indices_in_dist_mat = forward_lookup_table[object_ids]
+        indptr = np.empty(distance_matrix.indptr.shape[0] + 1, dtype=np.int64)
+        indptr[:-1] = distance_matrix.indptr
+        indptr[-1] = indptr[-2]
+        distance_matrix_extended = sp.csr_matrix(
+            (distance_matrix.data.astype(np.uint8), distance_matrix.indices, indptr),
+            shape=(distance_matrix.shape[0] + 1, distance_matrix.shape[1]),
+        )
+        rows = distance_matrix_extended[indices_in_dist_mat, :]
+
+        reverse_matrix_data = [np.array([], dtype=np.uint8)] * rows.shape[1]
+        reverse_matrix_col = [np.array([], dtype=np.int64)] * rows.shape[1]
+        nnz_array = np.zeros(rows.shape[1], dtype=np.int64)
+
+        for key, value in reverse_lookup_table.lookup.items():
+            reverse_matrix_data[key] = value.data
+            reverse_matrix_col[key] = value.col
+            nnz_array[key] = value.nnz
+
+        data = np.concatenate(reverse_matrix_data)
+        col = np.concatenate(reverse_matrix_col)
+        indptr = np.concatenate([np.array([0], dtype=np.int64), np.cumsum(nnz_array)])
+
+        reverse_matrix = sp.csr_matrix((data, col, indptr), shape=(rows.shape[1], reverse_lookup_table.size))
+        object_distance_matrix = rows * reverse_matrix
+        return object_distance_matrix
 
     def add_distance_matrix(
         self,
diff --git a/src/scirpy/tests/data/clonotypes_test_data/j_gene_test_data.h5ad b/src/scirpy/tests/data/clonotypes_test_data/j_gene_test_data.h5ad
new file mode 100644
index 000000000..43d588866
Binary files /dev/null and b/src/scirpy/tests/data/clonotypes_test_data/j_gene_test_data.h5ad differ
diff --git a/src/scirpy/tests/test_clonotypes.py b/src/scirpy/tests/test_clonotypes.py
index c0f6a0a39..06719cbaa 100644
--- a/src/scirpy/tests/test_clonotypes.py
+++ b/src/scirpy/tests/test_clonotypes.py
@@ -2,6 +2,7 @@
 import sys
 from typing import cast
 
+import anndata as ad
 import numpy as np
 import numpy.testing as npt
 import pandas as pd
@@ -339,3 +340,23 @@ def test_clonotype_convergence(adata_clonotype):
             categories=["convergent", "not convergent"],
         ),
     )
+
+
+def test_j_gene_matching():
+    from . import TESTDATA
+
+    data = ad.read_h5ad(TESTDATA / "clonotypes_test_data/j_gene_test_data.h5ad")
+
+    ir.tl.define_clonotype_clusters(
+        data,
+        sequence="nt",
+        metric="normalized_hamming",
+        receptor_arms="all",
+        dual_ir="any",
+        same_j_gene=True,
+        key_added="test_j_gene",
+    )
+
+    clustering = data.obs["test_j_gene"].tolist()
+    expected = ["0", "0", "0", "0", "0", "1", "1", "1", "1", "1", "1", "1", "1", "2", "2", "2", "2", "2"]
+    assert np.array_equal(clustering, expected)
diff --git a/src/scirpy/tl/_clonotypes.py b/src/scirpy/tl/_clonotypes.py
index 0f407a782..59e5cc94f 100644
--- a/src/scirpy/tl/_clonotypes.py
+++ b/src/scirpy/tl/_clonotypes.py
@@ -197,9 +197,10 @@ def define_clonotype_clusters(
     receptor_arms: Literal["VJ", "VDJ", "all", "any"] = "all",
     dual_ir: Literal["primary_only", "all", "any"] = "any",
     same_v_gene: bool = False,
+    same_j_gene: bool = False,
     within_group: Sequence[str] | str | None = "receptor_type",
     key_added: str | None = None,
-    partitions: Literal["connected", "leiden"] = "connected",
+    partitions: Literal["connected", "leiden", "fastgreedy"] = "connected",
     resolution: float = 1,
     n_iterations: int = 5,
     distance_key: str | None = None,
@@ -249,12 +250,19 @@ def define_clonotype_clusters(
 
     partitions
         How to find graph partitions that define a clonotype.
-        Possible values are `leiden`, for using the "Leiden" algorithm and
+        Possible values are `leiden`, for using the "Leiden" algorithm,
+        `fastgreedy` for using the "Fastgreedy" algorithm and
         `connected` to find fully connected sub-graphs.
 
-        The difference is that the Leiden algorithm further divides
+        The difference is that the Leiden and Fastgreedy algorithms further divide
         fully connected subgraphs into highly-connected modules.
 
+        "Leiden" finds the community structure of the graph using the
+        Leiden algorithm of Traag, van Eck & Waltman.
+
+        "Fastgreedy" finds the community structure of the graph according to the
+        algorithm of Clauset et al based on the greedy optimization of modularity.
+
     resolution
         `resolution` parameter for the leiden algorithm.
     n_iterations
@@ -289,6 +297,7 @@ def define_clonotype_clusters(
         receptor_arms=receptor_arms,  # type: ignore
         dual_ir=dual_ir,  # type: ignore
         same_v_gene=same_v_gene,
+        same_j_gene=same_j_gene,
         match_columns=within_group,
         distance_key=distance_key,
         sequence_key="junction_aa" if sequence == "aa" else "junction",
@@ -304,6 +313,8 @@ def define_clonotype_clusters(
             resolution_parameter=resolution,
             n_iterations=n_iterations,
         )
+    elif partitions == "fastgreedy":
+        part = g.community_fastgreedy().as_clustering()
     else:
         part = g.clusters(mode="weak")