diff --git a/src/neps/api.py b/src/neps/api.py
index cd2b26a7..c85b0225 100644
--- a/src/neps/api.py
+++ b/src/neps/api.py
@@ -238,7 +238,7 @@ def run(
     # Used to create the yaml holding information about the searcher.
     # Also important for testing and debugging the api.
     searcher_info = {
-        "searcher_name": searcher,
+        "searcher_name": str(searcher),
         "searcher_alg": searcher_alg,
         "user_defined_searcher": user_defined_searcher,
         "searcher_args_user_modified": False,
diff --git a/src/neps/optimizers/bayesian_optimization/acquisition_samplers/freeze_thaw_sampler.py b/src/neps/optimizers/bayesian_optimization/acquisition_samplers/freeze_thaw_sampler.py
index dfe7a60b..c103c993 100644
--- a/src/neps/optimizers/bayesian_optimization/acquisition_samplers/freeze_thaw_sampler.py
+++ b/src/neps/optimizers/bayesian_optimization/acquisition_samplers/freeze_thaw_sampler.py
@@ -21,7 +21,14 @@ def __init__(self, **kwargs):
         self.b_step = None
         self.n = None
         self.pipeline_space = None
+        # args to manage tabular spaces/grid
         self.is_tabular = False
+        self.sample_full_table = None
+        self.set_sample_full_tabular(True)  # sets flag that samples full table
+
+    def set_sample_full_tabular(self, flag: bool=False):
+        if self.is_tabular:
+            self.sample_full_table = flag
 
     def _sample_new(
         self, index_from: int, n: int = None, ignore_fidelity: bool = False
@@ -119,8 +126,11 @@ def __sample_single_new_tabular(index: int):
             _n = n if n is not None else self.SAMPLES_TO_DRAW
             _partial_ids = {conf["id"].value for conf in partial_configs}
             _all_ids = set(self.pipeline_space.custom_grid_table.index.values)
-            # accounting for unseen configs only
-            _n = min(_n, len(_all_ids - _partial_ids))
+
+            # accounting for unseen configs only, samples remaining table if flag is set
+            max_n = len(_all_ids) + 1 if self.sample_full_table else _n
+            _n = min(max_n, len(_all_ids - _partial_ids))
+            
             _new_configs = np.random.choice(
                 list(_all_ids - _partial_ids), size=_n, replace=False
             )