The above discussed efficiency, performance, and flexibility of the graph-based implementations of NSGA-II and NSGA-III for small molecule multi-objective optimisation as provided with this paper, allows the community to use these algorithms for practical use. In addition, these implementations can be used as future baselines and as starting points for future developments in this field. One such possible development would be to further reduce the amount of function calls through the use of contextual multiarmed bandits , or Gaussian processes to prune the amount of molecules presented to the evaluation step of the algorithms. Finally, the algorithms presented here can be integrated into the workflow for multi-objective tasks given to self-driving laboratories or other set-ups making use of active learning .