To explicitly demonstrate the relationship between descriptorbased and fingerprint-based rediscovery, we randomly selected a successful GB-BI run for fingerprint-based rediscovery of Troglitazone. From this specific run, we systematically sampled molecules, ensuring the inclusion of a single molecule (that had already been selected for fitness evaluation) from each occupied niche. Subsequently, we applied the two novel similarity metrics to these molecules, and compared those with the previously recorded fingerprint fitness values, as shown on the left-hand side of Figure . To quantitatively evaluate the correlation between descriptor-based similarities and fingerprint-based similar- ities, we calculated the Pearson correlation , which gauges the linear relationship between two variables, alongside Spearman's ρ 92 and Kendall's τ 93 . Both of these latter correlation measures are based on the rank of the data rather than the raw values. The results, presented at the top of Table , demonstrate a stronger correlation between USRCAT similarity and fingerprintbased similarity than the correlation between Zernike similarity and fingerprint-based similarity. Note that values coming from a conformer-aggregated similarity metric, as defined here, are strictly non-negative and remain bound between zero and unity (e.g. self-similarity).