Enhanced Thompson sampling by roulette wheel selection for screening ultralarge combinatorial libraries

Chemical space exploration has gained significant interest with the increasing availability of building blocks, enabling the creation of ultralarge virtual libraries containing billions or trillions of compounds. However, challenges remain in selecting the most suitable compounds for synthesis, especially in hit expansion. Thompson sampling, a probabilistic search method, has recently been proposed to improve efficiency by operating in reagent space rather than product space. Here, we address some of its limitations by introducing a roulette wheel selection method combined with a thermal cycling approach to balance greedy search and diversity-driven exploration. The effectiveness of this method is demonstrated through 109 queries against twenty distinct 1-million-compound libraries using ROCS.