One of the popular approaches is Bayesian optimisation. It is a stochastic optimisation method which is suitable for nonlinear and expensive-to-evaluate objective functions. Previous studies have shown that reaction optimisation with multiple objectives can be achieved efficiently using Bayesian optimisation. Schweidtmann et al. implemented Thompson Sampling Efficient Multi-Objective (TSEMO 10 ) for reaction optimisation in a flow reactor, both reactor productivity and environmental objectives were identified. The trade-off between these objectives was be found with multiple solutions to form a Pareto front. Later this algorithm was applied for multi-step reaction and separation, 8b biowaste conversion 11 and polymer synthesis. Müller et al. compared between three different multi-objective Bayesian optimisation algorithms and found TSEMO outperformed the other algorithms for the case study in terms of hypervolume improvement. However, when the reaction space contains a mix of continuous, discrete/categorical variables at the same time, implementing TSEMO to suggest next experimental condition can be computationally expensive. More recently, a new multi-objective Bayesian optimisation algorithm based on the q-Noisy Expected Hypervolume Improvement (qNEHVI) acquisition function was proposed. qNEHVI is an extension of expected hypervolume improvement acquisition function (qEHVI ) and is more robust to noise. The exact gradients of this acquisition function can be computed using modern auto-differentiation, therefore enabling efficient and effective optimisation using gradient-based optimisation methods. Recent studies implemented qEHVI and qNEHVI for butylpyridinium bromide synthesis, hexafluoroacetone heterogeneous synthesis, cross-electrophile coupling and materials synthesis, mostly with multiple continuous variables.