AIMSim can operate on datasets comprising tens of thousands of molecules, as discussed at length in Case Study #3 (Supporting Information S2). Execution time becomes a concern on datasets of this order of magnitude due to the underlying algorithm for comparisons being of O(𝑛 2 ) complexity. AIMSim implements multiprocessing and sampling techniques to reduce execution time. Using the multiprocess Python library , any number of processes can be spawned to divide the task of comparing molecules. AIMSim includes an automatic configuration option which uses heuristics (Supporting Information S6) to estimate if multiprocessing will result in faster execution and then configures itself accordingly.