In GB-BI, we use Bayesian optimisation with GB-EPI to select candidate molecules for evaluation. Bayesian optimisation employs the combination of a surrogate model (including uncertainties) and an acquisition function to balance exploration and exploitation, guiding the selection of candidate solutions. In qualitydiversity algorithms, this translates into an ensemble problem where the population must balance novelty search, behavioural diversity, and efficient exploitation of the fitness function. The recently developed Bayesian optimisation for the MAP-Elites algorithm (BOP-Elites) addresses this issue by constructing surro-gate models for the objective function and descriptor functions simultaneously and select candidate solutions to maximise a global acquisition function across niches.