diff --git a/python/python/lancedb/query.py b/python/python/lancedb/query.py
index c427820e8..53f56a2c0 100644
--- a/python/python/lancedb/query.py
+++ b/python/python/lancedb/query.py
@@ -82,6 +82,7 @@ if TYPE_CHECKING:
 
 T = TypeVar("T", bound="LanceModel")
 AnalyzePlanDistributedMetrics = Literal["aggregate", "per_worker", "full"]
+_SYNC_VECTOR_QUERY_TYPES = (list, tuple, np.ndarray, pa.Array, pa.ChunkedArray)
 
 # Number of rows a hybrid query returns when no limit was set on it. This
 # mirrors the default the Rust query builder applies to its sub-queries.
@@ -1024,10 +1025,12 @@ class LanceQueryBuilder(ABC):
                 fast_search=fast_search,
             )
 
-        if isinstance(query, list):
+        if isinstance(query, (list, tuple)):
             query = np.array(query, dtype=np.float32)
         elif isinstance(query, np.ndarray):
             query = query.astype(np.float32)
+        elif isinstance(query, (pa.Array, pa.ChunkedArray)):
+            query = np.array(query.to_pylist(), dtype=np.float32)
         else:
             raise TypeError(f"Unsupported query type: {type(query)}")
 
@@ -1049,7 +1052,7 @@ class LanceQueryBuilder(ABC):
             query = cls._query_to_vector(table, query, vector_column_name)
             return query, query_type
         elif query_type == "auto":
-            if isinstance(query, (list, np.ndarray)):
+            if isinstance(query, _SYNC_VECTOR_QUERY_TYPES):
                 return query, "vector"
             else:
                 conf = table.embedding_functions.get(vector_column_name)
@@ -1065,7 +1068,7 @@ class LanceQueryBuilder(ABC):
 
     @classmethod
     def _query_to_vector(cls, table, query, vector_column_name):
-        if isinstance(query, (list, np.ndarray)):
+        if isinstance(query, _SYNC_VECTOR_QUERY_TYPES):
             return query
         conf = table.embedding_functions.get(vector_column_name)
         if conf is not None:
diff --git a/python/python/lancedb/util.py b/python/python/lancedb/util.py
index d4533887b..3babc3872 100644
--- a/python/python/lancedb/util.py
+++ b/python/python/lancedb/util.py
@@ -309,7 +309,16 @@ def _query_vector_dim(query: Optional[Any]) -> Optional[int]:
         if query.ndim == 0:
             return None
         return query.shape[-1]
-    if isinstance(query, list) and query:
+    if isinstance(query, (pa.Array, pa.ChunkedArray)):
+        if len(query) == 0:
+            return None
+        if pa.types.is_fixed_size_list(query.type):
+            return query.type.list_size
+        if pa.types.is_list(query.type) or pa.types.is_large_list(query.type):
+            first = query[0].as_py()
+            return len(first) if first is not None else None
+        return len(query)
+    if isinstance(query, (list, tuple)) and query:
         first = query[0]
         if isinstance(first, (list, tuple, np.ndarray)):
             return len(first)
diff --git a/python/python/tests/test_table.py b/python/python/tests/test_table.py
index 095f0b911..7a4f5981c 100644
--- a/python/python/tests/test_table.py
+++ b/python/python/tests/test_table.py
@@ -3697,6 +3697,42 @@ def test_multiple_vector_columns(mem_db: DBConnection):
     assert result1["text"][0] != result2["text"][0]
 
 
+@pytest.mark.parametrize(
+    "query",
+    [
+        (0.0, 0.0),
+        pa.array([0.0, 0.0]),
+        pa.chunked_array([[0.0], [0.0]]),
+    ],
+)
+@pytest.mark.parametrize("query_type", ["auto", "vector"])
+def test_sync_search_accepts_supported_vector_containers(
+    mem_db: DBConnection, query, query_type
+):
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("small", pa.list_(pa.float32(), 2)),
+            pa.field("large", pa.list_(pa.float32(), 3)),
+        ]
+    )
+    table = mem_db.create_table(
+        "supported_vector_containers",
+        data=pa.table(
+            {
+                "id": [1, 2],
+                "small": [[0.0, 0.0], [1.0, 1.0]],
+                "large": [[1.0, 1.0, 1.0], [0.0, 0.0, 0.0]],
+            },
+            schema=schema,
+        ),
+    )
+
+    result = table.search(query, query_type=query_type).limit(1).to_arrow()
+
+    assert result["id"].to_pylist() == [1]
+
+
 def test_create_scalar_index(mem_db: DBConnection):
     vec_array = pa.array(
         [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], pa.list_(pa.float32(), 2)