In current molecular generative model benchmarks , typically either the median or the geometric mean of the objective is chosen as a stand-in aggregate fitness function. However, many fields of science and engineering make use of an alternative and arguably more sophisticated approach to multi-objective optimisation by searching for a set of so-called Pareto optimal solutions . All solutions in a Pareto optimal set are characterised by the fact that there are no other individual solutions that have a higher (or equal) fitness in all objective functions. Together, the set of a Centre for Integrative Neuroplasticity, University of Oslo, N-0316 Oslo, Norway; Email: jverhell@gmail.com Pareto optimal solutions form an optimal envelope in objective space known as the Pareto front, see Figure . Over the past two decades, a family of algorithms known as the non-dominated sorting genetic algorithms (NSGA) have been developed for finding Pareto fronts. In this paper, we provide the community with state-of-the-art and open-source implementations of the NSGA-II and NSGA-III algorithms based on a popular graph-based genetic algorithm (GB-GA) for molecular optimisation. A newer generation of NSGA algorithm, NSGA-III, which uses a more complex means of ensuring coverage of the entire Pareto front, was originally reported to be an improvement over NSGA-II. However, later analyses have shown that for a wide range of computational experiments NSGA-III does not consistently outperform NSGA-II in every use-case. Therefor we compare the performance of NSGA-III and NSGA-II on a set of small molecule multi-objective optimisation benchmarks, making use of the dominated hypervolume as a novel measure of the effectiveness in these type of problems. As a baseline, we make use of a stateof-the-art single-objective optimisation algorithm that employs the geometric mean as a surrogate aggregate fitness function. Whereas proprietary applications of NSGA-II to molecular design have been reported , there is a lack of open-source implementations of both NSGA-II and NSGA-III for the inverse design of small molecules. We anticipate that our results and the availability of the code will encourage the development of more powerful Pareto optimisation algorithms for chemistry as well as their widespread adoption in computer-assisted chemical design.