diff --git a/kernel_tuner/strategies/common.py b/kernel_tuner/strategies/common.py
index d01eae93..00700cd7 100644
--- a/kernel_tuner/strategies/common.py
+++ b/kernel_tuner/strategies/common.py
@@ -3,6 +3,7 @@
 from time import perf_counter
 
 import numpy as np
+import numbers
 
 from kernel_tuner import util
 from kernel_tuner.searchspace import Searchspace
@@ -109,8 +110,15 @@ def __call__(self, x, check_restrictions=True):
             self.runner.last_strategy_start_time = perf_counter()
 
         # get numerical return value, taking optimization direction into account
-        return_value = result[self.tuning_options.objective] or sys.float_info.max
-        return_value = return_value if not self.tuning_options.objective_higher_is_better else -return_value
+        return_value = result[self.tuning_options.objective]
+
+        if isinstance(return_value, numbers.Number):
+            if self.tuning_options.objective_higher_is_better:
+                # flip the sign if higher means better
+                return_value = -return_value
+        else:
+            # this is not a valid configuration, just return max
+            return_value = sys.float_info.max
 
         return return_value
 
diff --git a/kernel_tuner/strategies/simulated_annealing.py b/kernel_tuner/strategies/simulated_annealing.py
index dce929b7..3a89ad54 100644
--- a/kernel_tuner/strategies/simulated_annealing.py
+++ b/kernel_tuner/strategies/simulated_annealing.py
@@ -87,20 +87,24 @@ def tune(searchspace: Searchspace, runner, tuning_options):
 
 def acceptance_prob(old_cost, new_cost, T, tuning_options):
     """Annealing equation, with modifications to work towards a lower value."""
-    error_val = sys.float_info.max if not tuning_options.objective_higher_is_better else -sys.float_info.max
+    error_val = sys.float_info.max
+    res = 0.0
     # if start pos is not valid, always move
     if old_cost == error_val:
-        return 1.0
+        res = 1.0
     # if we have found a valid ps before, never move to nonvalid pos
-    if new_cost == error_val:
-        return 0.0
+    elif new_cost == error_val:
+        res = 0.0
     # always move if new cost is better
-    if new_cost < old_cost:
-        return 1.0
+    elif new_cost < old_cost:
+        res = 1.0
     # maybe move if old cost is better than new cost depending on T and random value
-    if tuning_options.objective_higher_is_better:
-        return np.exp(((new_cost-old_cost)/new_cost)/T)
-    return np.exp(((old_cost-new_cost)/old_cost)/T)
+    else:
+        if tuning_options.objective_higher_is_better:
+            res = np.exp(((new_cost-old_cost)/new_cost)/T)
+        else:
+            res = np.exp(((old_cost-new_cost)/old_cost)/T)
+    return res
 
 
 def neighbor(pos, searchspace: Searchspace):