The performance of NSGA-II and NSGA-III for graph-based optimisation of molecules is encouraging. Both algorithms specialise in finding the optimal Pareto front and our benchmarks show that this approach is superior compared to GB-EPI (which optimises the geometric mean directly). In line with analyses of purely numerical benchmarks found in the literature, NSGA-III does not always outperform NSGA-II in our chemical benchmarks, indicating that the two algorithm produce similar results according to this metric. Throughout all the benchmarks presented in this paper however, NSGA-III seems to be the most efficient in its use of function calls. Notably, and in contrast to single objective optimisation, the higher performing algorithms NSGA-II and NSGA-III have a higher and faster increasing internal similarity in their evolutionary populations than the baseline.