As the population size increases, more calculations are performed, which decreases speed-up (Figure ). The speedup was similar among the 3 chemical properties (ESI Table ). Figure shows the balance between coverage and speedup. There is a negative correlation, indicating that the best population size depends on the optimization task. If the goal is to find many highperforming candidates and the computation cost is not important, then a population size of 96 is recommended. If computational cost is essential, then a population size of 16 is suggested. The best balance of coverage and speedup is a population size of 32, since it has similar speedups to 24 and 28 individual populations but with higher coverage. Examination of this coveragespeedup trade-off for each individual chemical property (ESI Figure ) is consistent with this result, suggesting a population size of 32 gives the best balance across multiple chemical space topographies.