GB-BI, utilising the ECFP4 representation for the surrogate model and the posterior mean as an acquisition function, achieves a perfect rediscovery rate with, on average, 629 fitness function calls required. This marks a substantial improvement compared to GB-EPI, which is approximately 23 times less efficient than GB-BI, and GB-GA, which is roughly 38 times less efficient than GB-BI, as indicated in Table . Notably, GB-GA encounters stagnation issues, necessitating a minimum of 3 searches for successful rediscovery with at least 99% certainty. Factoring in this requirement would escalate the necessary number of fitness function calls for GB-GA to about 72,000, making it roughly 115 times less efficient than GB-BI. Recently, it has been asserted 89 that novel generative molecular models must demonstrate a clear advantage over genetic algorithms to be considered impactful in advancing the research field. GB-BI and GP-EPI unequivocally meet this GA criterion.