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 predefined computational budget, and the exclusive application of surrogate models to fitness functions. The latter aspect is reminis-cent 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 descrete 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 wth a surrogate-assisted quality-diversity algorithm.