The field of optimisation has seen a renaissance with the development of accurate machine learning models. Machine learning is a statistical method that relies on the relationships between input parameters and target outputs, and can be applied to model, optimise, and evaluate a target system. The integration of machine learning and optimisation has led to a new class of optimisation algorithms termed Bayesian optimisation. The Bayesian method is an active learning approach that applies a sequential strategy to solve optimisation problems and has been used to find the optima of complex functions in chemistry, biology, and materials science. A summary of different optimisation methods and their associated hyperparameters is summarised in Table . In this work, we outline the principles of Bayesian optimisation, including the mathematical foundation and choice of surrogate and acquisition functions. We examine how optimisation has been implemented in the chemical sciences, particularly focusing on automated research workflows. We discuss state-of-the-art approaches for handling small, noisy, or high-dimensional datasets. Finally, we outline the opportunities for developments in hybrid algorithms for robust and efficient searches.