We have introduced the strategy of Bayesian optimisation for accelerating discoveries in the chemistry domain. The key concepts are a surrogate model to estimate the probability distribution of the unseen objective function and an acquisition function to determine which point to sample next. We compared the performance of numerical (gradient descent), heuristic (simulated annealing) and statistical approaches (Bayesian optimisation) on toy optimisation problems, highlighting the potential for significant efficiency gains. Bayesian optimisation is starting to be adopted to aid the discovery of optimal molecules/materials and accelerating experimental characterisation of structures and properties. Despite this progress, several challenges and opportunities remain. Real experimental data is often noisy and this must be considered for efficient searches. In many setups, evaluating a sample can be highly costly and involve multiple observables. Parallel and multi-objective optimisation approaches have been developed that can overcome such challenges but currently require careful consideration of hyperparameters tailored for specific problems. An understanding of the range of chemical problems and the suitable methods in each case will facilitate the general application of Bayesian optimisation in chemical discoveries.