diff --git a/openmc/deplete/abc.py b/openmc/deplete/abc.py
index 8ba083b58..2b31bd3a3 100644
--- a/openmc/deplete/abc.py
+++ b/openmc/deplete/abc.py
@@ -848,10 +848,10 @@ class Integrator(ABC):
         return (self.operator.prev_res[-1].time[0],
                 len(self.operator.prev_res) - 1)
 
-    def _apply_keff_search_control(self, step_index, bos_conc):
+    def _apply_keff_search_control(self, bos_conc):
         """Apply keff search control to beginning-of-step concentrations."""
         x = deepcopy(bos_conc)
-        return self._keff_search_control.search_for_keff(x, step_index)
+        return self._keff_search_control.search_for_keff(x)
 
     def _restore_keff_search_control(self, res):
         """Restore keff search control from restart results."""
@@ -870,7 +870,7 @@ class Integrator(ABC):
         if step_index > 0 or self.operator.prev_res is None:
             if self._keff_search_control is not None and source_rate != 0.0:
                 bos_conc, keff_search_root = self._apply_keff_search_control(
-                    step_index, bos_conc)
+                    bos_conc)
             else:
                 keff_search_root = None
             bos_conc, res = self._get_bos_data_from_operator(
@@ -953,7 +953,7 @@ class Integrator(ABC):
             if output and final_step and comm.rank == 0:
                 print(f"[openmc.deplete] t={t} (final operator evaluation)")
             if self._keff_search_control is not None and source_rate != 0.0:
-                n, keff_search_root = self._apply_keff_search_control(i + 1, n)
+                n, keff_search_root = self._apply_keff_search_control(n)
             else:
                 keff_search_root = None
             res_final = self.operator(n, source_rate if final_step else 0.0)
diff --git a/openmc/deplete/keff_search_control.py b/openmc/deplete/keff_search_control.py
index ab656a4f1..8837dda3a 100644
--- a/openmc/deplete/keff_search_control.py
+++ b/openmc/deplete/keff_search_control.py
@@ -41,15 +41,13 @@ class _KeffSearchControl:
         self.search_kwargs['x_min'] = bracket[0]
         self.search_kwargs['x_max'] = bracket[1]
 
-    def search_for_keff(self, x, step_index):
+    def search_for_keff(self, x):
         """Perform keff search and update the atom density vector.
 
         Parameters
         ----------
         x : list of numpy.ndarray
             Current atom density vector (atoms per material)
-        step_index : int
-            Current depletion step index
 
         Returns
         -------