For example, a benchmark containing over one million everyday objects enabled comparisons of image recognition models on the same easily-accessible dataset. Much of the rapid progress in the field of image classification over the last ten years can be attributed to this benchmark. Instead of images, our benchmarks are models of reactions that provide predictions of reaction outcomes (e.g., yield) given reaction conditions. Our benchmarks act like virtual laboratories in which researchers can test the efficiency of each strategy through virtual experiments. We chose benchmarks that had already been validated experimentally, so anyone could use them without needing to run laboratory experiments. Each benchmark represents a different aspect of the chemical development process. The benchmarks present the challenges of process development, first with a S N Ar reaction run in a flow chemistry reactor and, then, selecting the optimal catalyst and ligand for a Pd-catalysed cross-coupling reaction. We note that a contemporaneous study, which also enables benchmarking of different optimisation strategies, aims at a broader range of experimental disciplines in chemistry and material science. Ours focuses specifically on chemical reactions, and we are the first to include extensive comparisons of different strategies.