With an increasing number of optimization algorithms, an effort to benchmark their performance is required. Felton et al. have highlighted the fact that the ability of an algorithm to perform well in a specific task may not translate universally to other problems, thus a specific optimization algorithm for chemical reaction optimization may have different performances depending on the nature of the target variables, objectives and chemical reaction. Also, the computational time required to execute an algorithm varies and it should be taken in consideration to select the most appropriate variation for each case study. To benchmark different optimization algorithms Felton et al. released Summit, a Python module containing several optimization algorithms and two benchmark in-silico models to compare the performance of algorithms. Initially, the benchmarking models included in Summit were a kinetic model for the SNAr reaction of difluronitrobenzene with pyrrolidine, and neural network forward model for the prediction of yield of diphenylamine in a Pd-catalysed C-N cross coupling reaction trained on a previously published data set containing 96 unique set of reaction conditions. The optimization for the SNAr reaction included four continuous variables and two optimization objectives, while the C-N cross coupling included three continuous variables, two categorical variables and two optimization objectives. The optimization algorithms used during the optimization included non-ML algorithms (Nelder Mead, SNOBFIT), BO algorithms (Gryffin, SOBO and TSEMO) and DRO a pretrained reinforcement learning agent algorithm. For the optimization of SNAr reaction, BO methods were superior to any other of the algorithms, reaching a higher HV in a smaller amount of iterations. When the BO algorithms were compared, TSEMO outperformed by a significant margin against Gryffin and SOBO. For the C-N cross-coupling, all models had a similar hypervolume (HV) performance including a random search of reaction conditions, due to the small parametric space for the selected categorical variable. Müller et al. also conducted benchmarking in-silico study for 6 different chemical reactions using previously reported kinetic models. In this case three distinct BO algorithms (TSEMO, ParEGO, EIM-EGO) and a genetic algorithm (NSGA-II) were compared. They demonstrated that BO methods outperformed non-BO methods such as NSGA-II, which is consistent with the Felton et al. studies earlier.