diff --git a/bofire/runners/run.py b/bofire/runners/run.py
index 89729bf2d..9df64e6fe 100644
--- a/bofire/runners/run.py
+++ b/bofire/runners/run.py
@@ -23,7 +23,7 @@ def _single_run(
     strategy_factory: StrategyFactory,
     n_iterations: int,
     metric: Callable[[Domain, pd.DataFrame], float],
-    n_candidates_per_proposals: int,
+    n_candidates_per_proposals: Optional[int],
     safe_interval: int,
     initial_sampler: Optional[
         Union[Callable[[Domain], pd.DataFrame], pd.DataFrame]
@@ -70,11 +70,13 @@ def autosafe_results(benchmark):
         # pd.concat() changes datatype of str to np.int32 if column contains whole numbers.
         # column needs to be converted back to str to be added to the benchmark domain.
         strategy.tell(XY)
+        assert isinstance(strategy.experiments, pd.DataFrame)
         metric_values[i] = metric(strategy.domain, strategy.experiments)
         pbar.set_description(f"Run {run_idx}")
         pbar.set_postfix({"Current Best:": f"{metric_values[i]:0.3f}"})
         if (i + 1) % safe_interval == 0:
             autosafe_results(benchmark=benchmark)
+    assert isinstance(strategy.experiments, pd.DataFrame)
     return strategy.experiments, pd.Series(metric_values)
 
 
@@ -84,7 +86,7 @@ def run(
     n_iterations: int,
     metric: Callable[[Domain, pd.DataFrame], float],
     initial_sampler: Optional[Callable[[Domain], pd.DataFrame]] = None,
-    n_candidates_per_proposal: int = 1,
+    n_candidates_per_proposal: Optional[int] = None,
     n_runs: int = 5,
     n_procs: int = 5,
     safe_interval: int = 1000,
diff --git a/bofire/strategies/predictives/botorch.py b/bofire/strategies/predictives/botorch.py
index 71c48c67e..dadab03ad 100644
--- a/bofire/strategies/predictives/botorch.py
+++ b/bofire/strategies/predictives/botorch.py
@@ -403,7 +403,7 @@ def _optimize_acqf_continuous(
             )
         return candidates, acqf_vals
 
-    def _ask(self, candidate_count: int) -> pd.DataFrame:  # type: ignore
+    def _ask(self, candidate_count: Optional[int] = None) -> pd.DataFrame:
         """[summary]
 
         Args:
@@ -413,6 +413,7 @@ def _ask(self, candidate_count: int) -> pd.DataFrame:  # type: ignore
             pd.DataFrame: [description]
 
         """
+        candidate_count = candidate_count or 1
         assert candidate_count > 0, "candidate_count has to be larger than zero."
         if self.experiments is None:
             raise ValueError("No experiments have been provided yet.")