To enhance the discriminative power of these benchmarks, we limit the maximum number of fitness function calls to 5000 per run and gather statistics of ten runs for each possible combination of molecular representation and acquisition function. In addition, to increase real-world relevance, we filter out molecules that fail at Veber's rule . In quality-diversity algorithms, like GB-BI, there are several relevant metrics to track the performance of an algorithm. These metrics include the maximum and mean fitness within the evolutionary population, the QD-score (sum of fitness values in the evolutionary population), and the coverage of the archive (percentage of niches containing a molecule). Additionally, we monitor metrics assessing the accuracy of the surrogate fitness model, including the maximum prediction error, mean squared error (MSE), and mean absolute error (MAE). Each of these metrics is calculated for every generation across all GB-BI runs analysed in this paper.