Strikingly, iterative similarity-based acquisition (not requiring any machine learning in principle) also yields higher enrichment than 'one-shot' virtual screening across the board, although lower than deep learning. Especially in early iterations, similarity-based acquisition seems to be highly effective, even when compared to the best performing deep learning methods. This suggests that the choice of the acquisition function (e.g., deep learning vs similarity-based) also needs to consider the total 'budget' of molecules to be tested experimentally. Finally, exploration (selecting the most uncertain molecules) yields the fewest hits, in line with previous work .