diff --git a/benchmarks/benchmarks/h5py_indexing.py b/benchmarks/benchmarks/h5py_indexing.py
new file mode 100644
index 000000000..4f18b9548
--- /dev/null
+++ b/benchmarks/benchmarks/h5py_indexing.py
@@ -0,0 +1,25 @@
+from __future__ import annotations
+
+import numpy as np
+
+from anndata._core.index import _process_index_for_h5py
+
+
+class ProcessIndexForH5py:
+    param_names = ("size", "scenario")
+    params = (
+        [10_000, 100_000, 1_000_000],
+        ["sorted_unique", "unsorted_unique", "duplicate_heavy"],
+    )
+
+    def setup(self, size, scenario):
+        rng = np.random.default_rng(0)
+        if scenario == "sorted_unique":
+            self.idx = np.arange(size, dtype=np.int64)
+        elif scenario == "unsorted_unique":
+            self.idx = rng.permutation(size).astype(np.int64)
+        else:
+            self.idx = rng.integers(0, max(1, size // 10), size=size, dtype=np.int64)
+
+    def time_process_index_for_h5py(self, size, scenario):
+        _process_index_for_h5py(self.idx)
diff --git a/src/anndata/_core/index.py b/src/anndata/_core/index.py
index 92359ab70..645fb56f8 100644
--- a/src/anndata/_core/index.py
+++ b/src/anndata/_core/index.py
@@ -455,7 +455,7 @@ def _subset_dataset(
 ) -> np.ndarray:
     order: tuple[NDArray[np.integer] | slice, ...]
     inv_order: tuple[NDArray[np.integer] | slice, ...]
-    order, inv_order = zip(*map(_index_order_and_inverse, subset_idx), strict=True)
+    order, inv_order = zip(*map(_process_index_for_h5py, subset_idx), strict=True)
     # check for duplicates or multi-dimensional fancy indexing
     array_dims = [i for i in order if isinstance(i, np.ndarray)]
     has_duplicates = any(len(np.unique(i)) != len(i) for i in array_dims)
@@ -465,25 +465,7 @@ def _subset_dataset(
         # For multi-dimensional indexing, bypass the sorting logic and use original indices
         return _safe_fancy_index_h5py(d, subset_idx)
     # from hdf5, then to real order
-    return d[order][inv_order]
-
-
-@overload
-def _index_order_and_inverse(
-    axis_idx: NDArray[np.integer] | NDArray[np.bool_],
-) -> tuple[NDArray[np.integer], NDArray[np.integer]]: ...
-@overload
-def _index_order_and_inverse(axis_idx: slice) -> tuple[slice, slice]: ...
-def _index_order_and_inverse(
-    axis_idx: _Index1DNorm,
-) -> tuple[_Index1DNorm, NDArray[np.integer] | slice]:
-    """Order and get inverse index array."""
-    if not isinstance(axis_idx, np.ndarray):
-        return axis_idx, slice(None)
-    if axis_idx.dtype == bool:
-        axis_idx = np.flatnonzero(axis_idx)
-    order = np.argsort(axis_idx)
-    return axis_idx[order], np.argsort(order)
+    return d[order][tuple(slice(None) if i is None else i for i in inv_order)]
 
 
 @overload
@@ -503,16 +485,8 @@ def _process_index_for_h5py(
     if idx.dtype == bool:
         idx = np.flatnonzero(idx)
 
-    # For h5py fancy indexing, we need sorted indices
-    # But we also need to track how to reverse the sorting
     unique, inverse = np.unique(idx, return_inverse=True)
-    return (
-        # Has duplicates - use unique + inverse mapping approach
-        (unique, inverse)
-        if len(unique) != len(idx)
-        # No duplicates - just sort and track reverse mapping
-        else _index_order_and_inverse(idx)
-    )
+    return unique, inverse
 
 
 def _safe_fancy_index_h5py(
diff --git a/tests/test_backed_hdf5.py b/tests/test_backed_hdf5.py
index 34571793c..ce6e17ddc 100644
--- a/tests/test_backed_hdf5.py
+++ b/tests/test_backed_hdf5.py
@@ -423,10 +423,54 @@ def h5py_test_data(tmp_path):
     return h5_path, test_data
 
 
+@pytest.mark.parametrize(
+    ("idx", "expected"),
+    [
+        pytest.param(
+            np.array([0, 1, 2], dtype=np.int64),
+            (np.array([0, 1, 2], dtype=np.int64), np.array([0, 1, 2])),
+            id="sorted_unique",
+        ),
+        pytest.param(
+            np.array([3, 1, 0, 2], dtype=np.int64),
+            (np.array([0, 1, 2, 3], dtype=np.int64), np.array([3, 1, 0, 2])),
+            id="unsorted_unique",
+        ),
+        pytest.param(
+            np.array([2, 2, 5, 3, 8, 10, 8], dtype=np.int64),
+            (
+                np.array([2, 3, 5, 8, 10], dtype=np.int64),
+                np.array([0, 0, 2, 1, 3, 4, 3]),
+            ),
+            id="duplicate_heavy",
+        ),
+        pytest.param(
+            np.array([True, False, True, False], dtype=bool),
+            (np.array([0, 2], dtype=np.int64), np.array([0, 1])),
+            id="boolean_mask",
+        ),
+        pytest.param(
+            np.array([], dtype=np.int64),
+            (np.array([], dtype=np.int64), np.array([], dtype=np.int64)),
+            id="empty",
+        ),
+    ],
+)
+def test_process_index_for_h5py(idx, expected):
+    from anndata._core.index import _process_index_for_h5py
+
+    processed, reverse = _process_index_for_h5py(idx)
+    expected_processed, expected_reverse = expected
+
+    np.testing.assert_array_equal(processed, expected_processed)
+    np.testing.assert_array_equal(reverse, expected_reverse)
+
+
 @pytest.mark.parametrize(
     ("indices", "description"),
     [
         pytest.param((np.array([0, 1, 0, 2]),), "single_dimension_with_duplicates"),
+        pytest.param((np.array([3, 1, 0, 2]),), "single_dimension_unsorted_unique"),
         pytest.param(
             (np.array([0, 1, 2]), np.array([1, 2])), "multi_dimensional_no_duplicates"
         ),
@@ -450,6 +494,7 @@ def h5py_test_data(tmp_path):
             (np.array([0, 1, 0]), [1, 2]), "mixed_indexing_with_slices_and_lists"
         ),
         pytest.param((np.array([3, 1, 3, 0, 1]),), "unsorted_indices_with_duplicates"),
+        pytest.param((np.array([], dtype=np.int64),), "empty_indices"),
     ],
 )
 def test_safe_fancy_index_h5py_function(h5py_test_data, indices, description):