In the next section, we choose to use a GP model with UCB acquisition function, with a cycle size of 200 and a diverse set of starting compounds. The overall accuracy of the chosen regression model (using β = 10), following training on 5 % of the dataset, is 0.97 pK units (Figure ), which is competitive with typical models used in active learning with fingerprint-based representations. Figure shows a similar UMAP projection as in Figure , but now only showing compounds acquired by our chosen active learning model in the first (left) and final (right) cycles. We observe both a wide exploration of the chemical space, which is important to increase diversity in the final set, and a focusing of the explored regions in the final cycle to compounds with a higher predicted binding affinity, which is important for the use of the model to identify strong binders.