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In its simplest form, pooled testing works by compositing a set of individual specimens (e.g., blood or urine) into a common pool. If the pool tests negative, all individuals within it are diagnosed as negative. If the pool tests positive, retesting is needed to decode the positive individuals from the negative individuals. Traditionally, pooled testing has assumed that each individual has the same probability of being positive. However, this assumption is often unrealistic, especially when known risk factors can be used to measure distinct probabilities of positivity for each individual. In this paper, we investigate new pooled\u2010testing algorithms that exploit the heterogeneity among individual probabilities and subsequently reduce the total number of tests needed, while maintaining accuracy levels similar to standard algorithms that do not account for heterogeneity. We apply these algorithms to data from the Infertility Prevention Project, a nationally implemented program supported by the Centers for Disease Control and Prevention. 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