in which the helper function log h (•) is a numerically stable implementation of the composite function log • h. Before BOP-Elites, the Surrogate-Assisted Illumination (SAIL) algorithm introduced the idea of using surrogate models to efficiently explore and map a design space based on user-defined features. SAIL differs from BOP-Elites in several key ways, including reliance on the UCB acquisition function, use of a pre-defined computational budget, and the exclusive application of surrogate models to fitness functions. The latter aspect is reminiscent of Bayesian optimisation as applied in this paper, however, it is important to emphasise that SAIL was designed specifically for continuous optimisation problems. A major distinction between molecular optimisation and continuous optimisation lies in how fitness is improved: molecular optimisation often requires specific mutations or crossovers to enhance fitness, while continuous spaces allow for smoother transitions between solutions. BOP-Elites, in contrast, showed promise in the discrete optimisation setting by making use of EI as an acquisition function. Hence, in this paper, we will refer to BOP-Elites as the basis for our exploration of chemical space with a surrogate-assisted qualitydiversity algorithm.