diff --git a/binchicken/workflow/scripts/target_elusive.py b/binchicken/workflow/scripts/target_elusive.py
index c5f467bb..6a947595 100755
--- a/binchicken/workflow/scripts/target_elusive.py
+++ b/binchicken/workflow/scripts/target_elusive.py
@@ -30,23 +30,29 @@ def get_clusters(
         # Set to 2 to produce paired edges
         MAX_COASSEMBLY_SAMPLES = 2
 
-    logging.info("Choosing preclusters based on distances")
+    logging.info("Processing distances...")
     best_samples = np.argsort(distances, axis=1)[:, :PRECLUSTER_SIZE]
     chosen_samples = [(samples[i], list(np.array(samples)[b[b != i]])) for i, b in enumerate(best_samples)]
+
+    sample_combinations = (
+        pl.DataFrame({"cluster_size": range(1, MAX_COASSEMBLY_SAMPLES)})
+        .with_columns(
+            sample_combinations = pl.col("cluster_size").map_elements(
+                lambda x: [i for i in itertools.combinations(range(PRECLUSTER_SIZE-1), x)],
+                return_dtype=pl.List(pl.List(pl.Int64)),
+                )
+            )
+        .explode("sample_combinations")
+        .select("sample_combinations")
+    )
+
+    logging.info("Choosing preclusters based on distances")
     with pl.StringCache():
         preclusters = (
             pl.DataFrame(chosen_samples, schema={"sample": pl.Categorical, "samples": pl.List(pl.Categorical)})
             .with_columns(length = pl.col("samples").list.len())
-            .join(pl.DataFrame({"cluster_size": range(1, MAX_COASSEMBLY_SAMPLES)}), how="cross")
-            .with_columns(
-                samples_combinations = pl.struct(["length", "cluster_size"]).map_elements(
-                    lambda x: [i for i in itertools.combinations(range(x["length"]), x["cluster_size"])],
-                    return_dtype=pl.List(pl.List(pl.Int64)),
-                    )
-                )
-            .select("sample", "samples", "samples_combinations")
-            .explode("samples_combinations")
-            .with_columns(pl.col("samples").list.gather(pl.col("samples_combinations")))
+            .join(sample_combinations, how="cross")
+            .with_columns(pl.col("samples").list.gather(pl.col("sample_combinations")))
             .select(
                 samples = pl.concat_list("sample", "samples")
                     .cast(pl.List(str))