Foraging is a search process common to mobile organisms, and foraging paths commonly exhibit statistical patterns akin to Lévy walks. There may be common factors and benefits underlying these patterns, but investigations are hindered by difficulty in assessing and manipulating search environments and task conditions. In the present study, a simple foraging game was developed to isolate and manipulate two factors hypothesized to make Lévy walks adaptive search strategies—sparsity, and spatial clustering of targets in the search environment. Players navigated a fuel-limited ship over a 2D grid to find as many targets as possible, rendered as asteroids in outer space. Over 1800 participants were recruited to play using Amazon’s Mechanical Turk, in order to widely sample the parameter space defined by degrees of target sparsity and clustering. Observed search paths resembled Lévy walks with memory, and those of high performers were found to vary adaptively with clustering, but not sparsity. Results indicate that Lévy-like walks can emerge from search strategies and algorithms adapted to environments with clustered resources.