Visual search involves scanning the surroundings to find objects of interest amidst a clutter of irrelevant distractors. A commonly accepted theory proposes that pop out visual search is governed by a winner-take-all (WTA) competition between contextually modulated cells that form a saliency map. Yet, research indicates that WTA's capacity to gather information even from large populations, has limitations, casting doubt on its role in pop out visual search. In our investigation, we performed a modeling study to assess the accuracy of WTA in detecting the distinctive object in a pop out task. We examined two algorithms: a single-best-cell WTA, where a decision is made based on a single winning neuron, and a generalized population-based WTA, where the outcome is determined by a group of winning neurons. Our results show that in both scenarios, WTA performance cannot account for the high accuracy found in behavioral experiments. Specifically: WTA was significantly impacted by neuronal heterogeneity. While robust to heterogeneity, the generalized algorithm was affected by observed neuronal noise correlations. Our results indicate that traditional concepts regarding the mechanisms behind pop out visual search should be reconsidered. This study also points toward experimental efforts that could be undertaken to explore these mechanisms further.