The objectives in these benchmarks, as shown in Table , are either similarity metrics that measure the distance to the corresponding drug molecule, or specific properties such as the amount of rotatable bonds in a molecule, the topological polar surface area (TPSA) or the lipophilicity partition coefficient (log(P)). The similarity metrics are calculated using the Tanimoto similarity , of the fingerprints of the target and the generated candidate molecule. The fingerprints used here are either extendedconnectivity fingerprints (ECFP/FCFP) which encode molecular structures in terms of concentric atomic neighbourhoods, or atom-pair fingerprints (AP) which encode molecules based on their atom pairs and their bond distance. The main advantage of fingerprint-based similarities compared to more involved similar-Table Overview of the multi-objective optimisation benchmarks adapted from the Guacamol suite. The upper row of each task represents the values calculated for each objective. The lower rows show the modifiers applied to each of these values. The fingerprints used to calculate the similarities are denoted as arguments of the Tanimoto function, the parameters used for the modifiers are displayed as arguments of the corresponding functions. The CNS function calculates the central nervous system desirability score (high blood-brain-barrier permeability and low toxicity potential) as proposed by Pfizer 51 . MaxGaussian(7.0, 1.0) MaxGaussian(95.0, 20.0) Gaussian(1.0, 1.0) ity measures is that they can be rapidly calculated and inherently represent the presence or absence of molecular substructures or atom pairs. The raw scores obtained from similarity or property measurements are post-processed by modifier functions that map the scores to the [0, 1] interval and allow the objective to be fine-tuned. The modifier functions used in this paper are Clipped(value), Gaussian(mean, variance), MinGaussian(mean, variance), and MaxGaussian(mean, variance). The Clipped modifier is a thresholded modifier in which values above a given threshold are mapped to one, while values below threshold decrease linearly to zero. The Gaussian modifiers target a specific value, returning high scores when the underlying value is near the target. The Min and Max versions of this modifier map the input value to one if it is lower or higher than the target value, respectively. For example, in the Fexofenadine benchmark a molecule with a Tanimoto similarity higher than 0.8, a TPSA above 90.0 and a log(P) below 4.0 would score perfectly on each objective. More information on the modifiers can be found in the supplementary information accompanying the Guacamol paper .