To recapitulate, GB-BI is an illumination algorithm for efficiently generating optimised small molecules combining ideas from genetic algorithms (GB-GA), quality-diversity algorithms (GB-EPI) and Gaussian processes for small molecule representations (GAUCHE) and Bayesian optimisation (BOP-Elites). In GB-GI, molecules are acted upon as molecular graphs for mutations and crossovers, and represented by either fingerprints or as a bagof characters based on SMILES or SELFIES for use in Gaussian processes. Mutations and crossovers are used to generate new molecules which are added to the evolutionary population, based on their comparative fitness with the current occupier of their physicochemical niche. The Gaussian processes are used to create a surrogate fitness function and only the molecules with the highest acquisition function value in their niche receive a fitness evaluation.