Olympus' plotting module then provides users with access to a series of visualizations of benchmark results. Here, we choose the num evals top k type boxplot, which constructs distributions of the number of yield evaluations needed for each experiment planner to identify a top-20 yield with respect to all 3696 measurements. For this dataset, a top-20 value roughly corresponds to a yield of ≥ 96%. The resulting boxplot is shown in Figure . It is immediately clear that the Bayesian optimizers (HyperOpt and BoTorch) perform significantly better than Random Search and Genetic on this task. HyperOpt is able to identify a top-20 yield on average within 79 ± 10 evaluations, while BoTorch is able to do the same within only 27 ± 2 evaluations. Ultimately, the insight gleaned from this case study should inform choices of experiment planning algorithm in future experiments.