Machine learning has recently assumed a prominent role in chemistry: predicting ADMET properties , supporting molecular dynamics simulations , and assisting in the design of small molecules without reverting to explicit rules or expert knowledge . However, training-free optimisation algorithms that comprehensively traverse and explore chemical space have been shown to be more efficient than their machine learning counterparts in discovering high-performing de novo molecules. Sometimes this search in chemical space reduces to an optimisation for a single property like melting point or protein binding affinity , but often there are additional requirements that make it necessary to optimise for additional properties such as low toxicity or high synthesizability . In the case that multi-objective optimisation is necessary, a trade-off between different (and possibly competing) optimisation objectives has to be defined.