This paper introduces two novel open-source and graph-based implementations of non-dominated sorting genetic algorithms, NSGA-II and NSGA-III, for small molecule multi-objective optimi-sation. The performance of these algorithms is compared to a single objective quality-diversity algorithm (GB-EPI) on four metrics: dominated hypervolume, maximal geometric mean, internal similarity and efficiency. Previous benchmarks for generative models of small molecules focused on the maximal geometric mean as a sole aggregate indicator of success in multi-objective optimisation. However, the Pareto front -the collection of optimal points in objective space -is not solely characterised by the geometric mean of a single molecule. In this paper we show that the size of the hypervolume dominated in objective space (with respect to the origin) is a useful, often more discriminative, alternative metric in generative model benchmarks.