In multi-objective problems, tracking the evolution of an algorithm or measuring the quality of a Pareto front with respect to a single parameter can be challenging. In previous benchmarking efforts for optimisation algorithms of small molecules, the geometric mean of the objectives has traditionally been used as both an aggregate objective and as a metric. From a technical point of view, the geometric mean is the exponential of the arithmetic mean of the log-transformed set of objective scores. As a consequence, the geometric mean for strictly positive values is sensitive to severe underperformance in any single objective, making it a relevant measure for many multi-objective optimisation problems. However, other indicators of the quality of Pareto fronts have been developed by the multi-objective optimisation community. One such metric is the dominated hypervolume , which we introduce to the domain of chemical optimisation as an alternative measure for multi-objective optimisation benchmarks.