In the optimisation community, the idea of "No-free lunch" is often cited. The principle is that an optimisation strategy that works well for one problem will inherently be bad for another. This implies that understanding the strengths and weaknesses of each optimisation strategy are important. Here, we have identified that Bayesian optimisation is an efficient way to overcome the challenges of reaction optimisation. We thank Alexander Pomberger for useful feedback on this manuscript. Summit was designed with the principles of simplicity and flexibility in mind. Previously, most machine learning strategies for reaction optimisation have been implemented in Matlab. Matlab offers the benefits of being relatively easy to learn and flexible enough to enable fast development of new ideas. However, we see strong opportunities in using python due its prevalence in data science and the availability of machine learning packages such as Tensorflow and PyTorch. By leveraging these packages, we can keep the code relatively simple. Summit has two main components: