A challenge in Computer-Assisted Drug Design (CADD) is to computationally generate or evaluate molecules that fit a given set of constraints. This process is not devoid of error: for instance, an inactive molecule can be predicted as active (false positive) or an active one can be predicted as inactive (false negative). From a drug design perspective, false positives are more pernicious due to the economic impact a wrong prediction can have. False negatives do not impact as directly but are important nonetheless: the next blockbuster could be any molecule wrongly skipped by computational solutions.