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The low prevalence effect, which posits that people are more likely to miss a present target when its prevalence rate is low, has important implications for real-world scenarios such as cancer screening and bomb detection. This effect has primarily been studied under full visibility; however, real-world scenarios often come with incomplete visibility. Occlusion and poor visibility introduce perceptual uncertainty, potentially altering how people decide whether a target is present. Here, we applied Bayesian decision theory to a visual search paradigm with partial occlusion, examining how target prevalence (prior) and the degree of occlusion (likelihood information) affect search decisions. Participants made target present/absent responses to target/distractor stimuli. In Experiment 1, all items were invisible, forcing participants to rely on trial feedback to learn the target's prevalence. Experiment 2 also provided trial feedback, but allowed either a small or large portion of the display to be visible. Target prevalence varied between blocks (high, 50%; low, 25%). Results showed that, when items were entirely hidden, participants learned to probability match the target's prevalence. However, when some items were visible, participants rarely responded present when the target was in the occluded region. Comparing the data with models (e.g., probability matching, Bayesian maximizing) revealed mixed strategies. This study introduces a novel method for investigating visual search under occlusion and suggests that, although people integrate prevalence and sensory input, their decisions are not fully Bayesian.