The results of the protein binding tasks for three independent runs of GB-BI, GB-EPI, and GB-GA are presented at the bottom of Table . GB-BI consistently outperforms both GB-EPI and GB-GA across all three tasks, achieving superior results in terms of both the minimum obtained docking score and the mean docking score for the 100 best compounds at the end of optimisation for each algorithm. To evaluate the quality-diversity effectiveness of GB-BI and GB-EPI, we calculate the QD-score 104 (the sum of all fitness values of the molecules present in the archive at the last generation) and the percentage of archive niches occupied by a molecule, known as archive coverage 105 . Since GB-GA is not a quality-diversity algorithm, we do not calculate the QD-score or archive coverage for it. The QD-score is a widely used qualitydiversity measure that assesses an algorithm's ability to populate its archive with diverse yet high-performing solutions. In the context of this paper, the archive represents a section of chemical space, and our results indicate that GB-BI demonstrates a significantly enhanced capacity to generate a variety of optimised molecules compared to the standard quality-diversity approach used in GB-EPI.