diff --git a/vectordb_bench/backend/dataset.py b/vectordb_bench/backend/dataset.py
index 62700b0fa..f90580dc6 100644
--- a/vectordb_bench/backend/dataset.py
+++ b/vectordb_bench/backend/dataset.py
@@ -220,10 +220,12 @@ def prepare(
         train_files = utils.compose_train_files(file_count, use_shuffled)
         all_files = train_files
 
-        gt_file, test_file = None, None
+        test_file = "test.parquet"
+        all_files.extend([test_file])
+        gt_file = None
         if self.data.with_gt:
-            gt_file, test_file = utils.compose_gt_file(filters), "test.parquet"
-            all_files.extend([gt_file, test_file])
+            gt_file = utils.compose_gt_file(filters)
+            all_files.extend([gt_file])
 
         if not self.data.is_custom:
             source.reader().read(
@@ -232,8 +234,10 @@ def prepare(
                 local_ds_root=self.data_dir,
             )
 
-        if gt_file is not None and test_file is not None:
+        if test_file is not None:
             self.test_data = self._read_file(test_file)
+
+        if gt_file is not None:
             self.gt_data = self._read_file(gt_file)
 
         prefix = "shuffle_train" if use_shuffled else "train"
diff --git a/vectordb_bench/backend/runner/serial_runner.py b/vectordb_bench/backend/runner/serial_runner.py
index 365641132..5b418c886 100644
--- a/vectordb_bench/backend/runner/serial_runner.py
+++ b/vectordb_bench/backend/runner/serial_runner.py
@@ -209,7 +209,8 @@ def search(self, args: tuple[list, pd.DataFrame]) -> tuple[float, float, float]:
             ideal_dcg = get_ideal_dcg(self.k)
 
             log.debug(f"test dataset size: {len(test_data)}")
-            log.debug(f"ground truth size: {ground_truth.columns}, shape: {ground_truth.shape}")
+            if ground_truth is not None:
+                log.debug(f"ground truth size: {ground_truth.columns}, shape: {ground_truth.shape}")
 
             latencies, recalls, ndcgs = [], [], []
             for idx, emb in enumerate(test_data):
@@ -228,9 +229,13 @@ def search(self, args: tuple[list, pd.DataFrame]) -> tuple[float, float, float]:
 
                 latencies.append(time.perf_counter() - s)
 
-                gt = ground_truth["neighbors_id"][idx]
-                recalls.append(calc_recall(self.k, gt[: self.k], results))
-                ndcgs.append(calc_ndcg(gt[: self.k], results, ideal_dcg))
+                if ground_truth is not None:
+                    gt = ground_truth["neighbors_id"][idx]
+                    recalls.append(calc_recall(self.k, gt[: self.k], results))
+                    ndcgs.append(calc_ndcg(gt[: self.k], results, ideal_dcg))
+                else:
+                    recalls.append(0)
+                    ndcgs.append(0)
 
                 if len(latencies) % 100 == 0:
                     log.debug(