Fitness To gain a more comprehensive understanding of how various molecular representations and acquisition functions influence the performance of GB-BI, we present a detailed analysis incorporating maximum fitness value, maximum error, rediscovery rate, and average fitness calls required for rediscovery. These insights are depicted in a series of heatplots showcased in Figure . Notably, these figures clearly reveal that only the posterior mean coupled to the ECFP4 fingerprint representation achieves a perfect rediscovery rate within the allocated budget of 5000 fitness function calls. Surprisingly, neither EI nor logEI outperform simpler acquisition functions like UCB or the posterior mean in terms of both maximum fitness or rediscovery rate. Moreover, a high maximum fitness score or a low maximum error does not necessarily correlate with a high rediscovery rate, underscoring the significance of evaluating multiple complimentary metrics when analysing the effectiveness of different quality-diversity methods for small molecule generation.