Since version 2, DrugEx enables multi-objective optimisation during RL. DrugEx offers three different MOO schemes: a parametric aggregation method and Pareto ranking-based schemes. The aggregation method is the parametric weighted sum (WS) which uses dynamic weights for each objective to especially reward compounds that perform well on the worst-performing objective(s) at each iteration. Pareto-based schemes do not combine multiple objectives into one but rather search for the best trade-off between them and the initial ranking of molecules is done based on the ranking of the Pareto frontiers. After assigning each molecule to a front, the compounds in each frontier are ranked based on a distance metric to increase the diversity of solutions. DrugEx proposes two distance metric formulations: the crowding distance (PRCD) and the Tanimoto distance (PRTD). For the latter we propose several subtly different ranking schemes all based on the Tanimoto distance. Detailed descriptions of three schemes are given in section S3. The WS is slightly faster than the Pareto-based methods, and more stable for many-objective optimisation as the number of Pareto-equivalent solutions increases with the number of objectives. On the other hand, the Pareto-based schemes enforce diversity in the ranking which is not the case for the WS.