One of the main assumptions of the main question presented to the participants in this study is that, over the course of their careers, medicinal chemists develop an expertise that is hardly quantifiable by other existing in silico metrics. In order to evaluate whether such is the case, we measure to what degree the learned compound scores correlate with other ligandbased properties that are commonly-used during optimization (e.g., drug-likeness, topological surface area, number of saturated rings). All properties considered were computed with the RDKit software package. A summary of the highest correlated properties (on an absolute scale) in the training data is presented in Figure . With Pearson correlation coefficients overall not surpassing the r = 0.4 threshold, we conclude that the learned scores are in fact providing a perspective on molecules that is orthogonal to what can be currently computed with other cheminformatics software routines. Among the most correlated properties we can find: drug-likeness, fingerprint density, the fraction of allylic oxidation sites, atomic contributions to the van der Waals surface area, or the Hall-Kier kappa value. For completeness, an extensive list of all of the properties computed as well as their correlations to the learned scores is also provided in Figure . ) ROC AUC curves for both MolSkill scores and QED values when tasked to discriminate between molecules from either ChEMBL or FDA-approved drugs from GDB17-extracted molecules