We first focus on the evaluation of the results provided by the first preliminary rounds for the study, which ultimately led us to pursue the subsequent production-level runs. This is followed by a quantitative evaluation of predictive model performance over the different rounds considered in the study. We then proceed to explore several areas where we believe the proposed scoring function can be practical. We study the relationship of the learned scoring function to other common in silico metrics in chemoinformatics and evaluate whether it can distinguish between chemical sets of different nature. We further investigate whether more precise learned chemical preferences can be rationalized via means of a fragment analysis and, finally, exemplify the usage of the proposed scoring function in biased molecular generation.