For its quality-diversity capabilities, GB-BI relies on the dataarchitecture and selection policies of GB-EPI, which in turn used MAP-Elites to solve the stagnation issues of GB-GA. To reliably outperform deep generative models for small molecule design, GB-EPI mimics diversity in biological evolution by assigning candidate solutions from a GB-GA to different niches depending on their characterising features. In each generation, the best performing candidate molecule in each of the individual niches is retained, creating a population of locally elite and diverse solutions that can be used as a resource to escape evolutionary stagnation. In GB-EPI, and hence also in GB-BI, users can choose which physicochemical properties of interest to use in spanning chemical space , and select the boundaries within which a grid of niches is created.