Significance We developed software called Candidate Explorer (CE) that uses a machine-learning algorithm to identify chemically induced mutations that are causative of screened phenotypes. CE determines the probability that a mutation will be verified as causative for a phenotype if the gene is independently targeted for knockout or recreation of the mutation. CE uses 67 parameters from the mapping data—including gene, mutation, genotype, allelism, and phenotype information—to determine the CE Score and verification probability. We used CE to evaluate ∼87,000 mutation/phenotype associations identified by flow cytometry screening of circulating immune cells from mutagenized mice: 1,279 genes representing 2,336 mutations were rated good or excellent candidates for causation of phenotypes. Many of these genes were not previously implicated in immunity.