To address the limited exploitation of fitness information in GB-GA and GB-EPI, GB-BI makes use of a surrogate model to guide the selection of new molecules, coming from mutations and crossovers, for fitness evaluation. A good surrogate model accurately approximates the true fitness landscape based on the data it observed and estimates its own uncertainties. In molecular machine learning, Bayesian neural networks and ensembles are sometimes used for property predictions with uncertainty estimation. However, in scenarios where the dataset is small, deep learning models face challenges in achieving out-ofdistribution generalisation. Gaussian processes are often a preferred alternative for the small data regime, because of their ability to perform exact Bayesian inference and their minimal need for manual determination of hyper-parameters.