In this manner, GB-BI aims to combine the stepping stones of quality-diversity algorithms with the functional call efficiency of Bayesian optimisation. Note that in GB-BI, exploration-vsexploitation is controlled by a combination of the specifics of the physicochemical archive, the accuracy of the Gaussian processes, and the chosen acquisition function. For near-optimal fitness values, GB-BI might suffer from numerically vanishing or otherwise uninformative acquisition function values and -as an unfortunate consequence -the reintroduction of stagnation issues. To avoid this in this paper, we will explore the combinations of nine different molecular representations (ECFP4, ECFP6, FCFP4, FCFP6, RDFP, APFP, TTFP, SMILES and SELFIES) and four different acquisition functions (EI, Posterior Mean, UCB, and logEI) for their efficiency in rediscovering existing molecules.