We made substantial updates to the analysis and plotting modules of our package. Table lists and describes the supported plot types which can be accessed via the Plotter module of Olympus. In summary, we support three main plot types: traces, boxplots, and scatterplots. Trace plots compare the optimization performance of experiment planners by showing the cumulative best objective function value, regret value, or candidate rank achieved by an experiment planner as a function of the number of transpired objective function evaluations. We also provide trace plots which depict the cumulative fraction of top-k candidates evaluated by the experiment planner. As an example, consider an optimization using the perovskites dataset, where the goal is to minimize the bandgap across a dataset of hybrid organic-inorganic perovskite materials. Upon completion of the optimization benchmark (here comparing Random Search and Bayesian optimization via the BoTorch planner), we can ask Olympus to visualize the fraction of top-10 perovskite materials discovered by each strategy as a function of the number of bandgap evaluations. For brevity, we assume we already have an Olympus database instance called database perovskites which contains the benchmark results. Figure shows the plot generated by the following code snippet. Boxplots summarize the results of an optimization benchmark in a concise manner, showing the distribution of some performance metric for a series of experiment planning strategies over multiple executions. In particular, summarizing the relative performance of multiple experiment planning strategies on a multi-objective optimization problem (i.e., the comparison of Pareto fronts) using a single scalar value is a valuable tool for a researcher to have available at their fingertips. One such metric is the hypervolume indicator, a set-quality indicator which measures the volume of the dominated portion of the objective space. This metric is of great interest in multi-objective optimization research as it maintains the desirable feature of strict Pareto compliance. We omit a detailed introduction to the hypervolume indicator, and refer the interested reader to other works, as well as SI Sec. S.5. In short, the hypervolume can be interpreted as the "size of the dominated space" and thus, experiment planning strategies which achieve the largest relative hypervolume can be considered the best performers. As an example, consider a multi-objective optimization experiment on the mult fonseca analytic benchmark surface. Olympus produces boxplots of the normalized hypervolume over repeated executions of the experiment. Figure shows the plot produced by the following code snippet. Olympus can also produce scatterplot visualizations of the Pareto front itself. This scatterplot can be accessed only for problems with 2 or 3 objectives. As an example, consider a comparison of Random Search and BoTorch on the mult fonseca surface. Both objectives of this surface are to be minimized.