The acquisition function is used to select a subset of the generated batch, 𝑋 𝐴 , to evaluate with the oracle, e.g. 𝑋 𝐴 ⊂ 𝑋; |𝑋 𝐴 | = 𝑁 acquired 𝑁 loops ≤ 𝑁 batch = |𝑋|, though we also explore iterative construction of this set. We investigate and compare the performance of two distinct strategies: greedy acquisition and UCB (Methods). We selected these strategies due to their varying approaches to balancing exploration vs exploitation and success in previous studies . Note that, in the context of the RL-AL with weight zero used for surrogate predictions model, random acquisition is equivalent to not using AL at all since no surrogate predictions are used for the RL update. UCB found 27% more scaffolds compared to greedy (116±8 for greedy vs 147±13 for UCB) with a similar Tanimoto diversity (0.205±0.005 for greedy vs 0.212±0.017 for UCB), both methods outperform random acquisition (Supporting ).