To assess the efficacy of GB-BI and GB-EPI algorithms in the context of USRCAT and Zernike-based Troglitazone rediscovery, we monitored the maximum and mean fitness within the evolutionary population, as well as the QD-score. The statistical summaries of these metrics across the final population of each run, are presented in Table . From these results, we can make the rather remarkable observation that neither GB-BI nor GB-EPI have managed to rediscover Troglitazone based on either USR-CAT or Zernike descriptors. Clearly, the descriptor-based rediscovery tasks are substantially more challenging than their finger-Table Performance evaluation of GB-BI and GB-EPI in the descriptorbased rediscovery tasks for Troglitazone, in terms of the mean and standard deviation of the maximum fitness and mean fitness of the evolutionary population and the QD-score. The presented statistics were obtained based on five independent runs of GB-BI and GB-EPI, each with a maximum budget of 1500 fitness calls, for both benchmarks. print equivalent. This might be due to the presence of activity cliffs -pairs of molecules with highly similar molecular graphs but displaying large differences in fitness -in descriptor-based rediscovery. Another striking observation, seen in both benchmarks, is the lack of significant difference in performance between GB-BI and GB-EPI in terms of either the maximum or mean fitness in the population. GB-BI does perform significantly better in terms of QD-score, see the right-hand side of Figure , which indicates that it manages to generate a larger and more diverse population of (comparatively) high-scoring molecules.