Visual search for a target is affected by visual similarity. Research on visual similarity has primarily focused on the high-level features of objects. Real-world objects are composed of low-level features that can be harder to measure and categorize. We have developed ObViS, an algorithm that measures the visual similarity of objects, based on Rao & Ballard (1995). ObViS calculates a high-dimensional vector that represents the low-level features of a real-world object. The algorithm was applied to a library of real-world object images in order to calculate the similarity of each object to every other object in the library. Two experiments evaluated the ability of our algorithm to predict the effects of visual similarity on visual search behavior.