Clutter is something that is encountered in everyday life, from a messy desk to a crowded street. Such clutter may interfere with our ability to search for objects in such environments, like our car keys or the person we are trying to meet. A number of computational models of clutter have been proposed and shown to work well for artificial and other simplified scene search tasks. In this paper, we correlate the performance of different models of visual clutter to human performance in a visual search task using natural scenes. The models we evaluate are Feature Congestion (Rosenholtz, Li, & Nakano, 2007), Sub-band Entropy (Rosenholtz et al., 2007), Segmentation (Bravo & Farid, 2008), and Edge Density (Mack & Oliva, 2004) measures. The correlations were performed across a range of target-centered subregions to produce a correlation profile, indicating the scale at which clutter was affecting search performance. Overall clutter was rather weakly correlated with performance (r ≈ 0.2). However, different measures of clutter appear to reflect different aspects of the search task: correlations with Feature Congestion are greatest for the actual target patch, whereas the Sub-band Entropy is most highly correlated in a region 12° × 12° centered on the target.