Choosing how to acquire the next iteration of molecules is arguably one of the most crucial factors in active learning, since it will determine hit allocation and what structure-activity information will be fed back into the model for future screening cycles. Here, we found that exploitation (selection of the best predicted molecules) and acquisition based on mutual information outperform all other acquisition functions, regardless of the dataset and the deep learning approach (Fig. ). Active learning achieves a remarkable increase in the number of hits compared to screening the same number of molecules in just one go ('one-shot'). For instance, active learning with exploitation leads to a two-to four-fold enrichment compared the corresponding 'one-shot' approach. Across all datasets and most molecule acquisition functions, ECFP-based models outperform graph-based models in hit enrichment. Graph neural networks, however, achieve a slightly higher prediction accuracy on the PKM2 and ALDH1 datasets (Supp. Table and Supp. Fig. ). Although graph-based methods tend to find less hits, they exhibit very similar behaviour to the ECFP-based methods in the relative performance of acquisition functions. Therefore, from here on, we will report mostly on the results of ECFP-based models (while those of graph neural networks can be found as Supplementary Information).