The normalized LogAUC of a random classifier tends to 0% as the numbers of positive-class molecules and negative-class molecules both go to infinity . However, whereas a typical retrospective docking campaign usually involves between 10 to 30 known ligands, curated datasets such as DUDE-Z often have targets with more than 100 ligands. To obtain a conditional probability distribution of normalized LogAUC produced by a random classifier given a specific number of positive-class molecules, we performed 1 billion simulations for each number of positives, ranging from 1 to 100, with all datasets maintaining a negative-to-positive ratio of 50:1. The empirical distributions obtained from these simulations (Supporting Information S3) allow us to compute the probability that a perfect random classifier scoring n positives and 50n negatives would, purely by chance, produce a normalized LogAUC greater than or equal to a given value.