  Chemical space is so large that brute force searches for new interesting
molecules are infeasible. High-throughput virtual screening via computer
cluster simulations can speed up the discovery process by collecting very large
amounts of data in parallel, e.g., up to hundreds or thousands of parallel
measurements. Bayesian optimization (BO) can produce additional acceleration by
sequentially identifying the most useful simulations or experiments to be
performed next. However, current BO methods cannot scale to the large numbers
of parallel measurements and the massive libraries of molecules currently used
in high-throughput screening. Here, we propose a scalable solution based on a
parallel and distributed implementation of Thompson sampling (PDTS). We show
that, in small scale problems, PDTS performs similarly as parallel expected
improvement (EI), a batch version of the most widely used BO heuristic.
Additionally, in settings where parallel EI does not scale, PDTS outperforms
other scalable baselines such as a greedy search, $\epsilon$-greedy approaches
and a random search method. These results show that PDTS is a successful
solution for large-scale parallel BO.
