In the last decade, advances in robotics platforms tailored to the chemical sciences have enabled the rise of automated experiments. These systems can screen large libraries of precursors and experimental conditions more rapidly and reproducibly than human researchers. A natural application of Bayesian optimisation is driving the selection of input parameters to minimise or maximise an experimental observable in automated chemical experiments.