In some fields, black-box optimisation starting from no prior information is necessary. In the chemistry domain however, we generally have some insights and expectations concerning the physical nature of our system and its variables. For Bayesian optimisation, this may involve the incorporation of physical laws, phase stability criteria, and intrinsic material property constraints to inform the surrogate model/ acquisition function and refine the search towards regions most likely to contain promising solutions. The integration of a structured probabilistic model that captures the expected physical behavior of the system proved successful for solving Ising problems in physics. Constrained Bayesian optimisation was used to improve the validity and quality of molecular candidates in a generative computational search. In the context of materials design, leveraging information on preferred coordination environments, valency, and known synthetic accessibility, could guide the optimisation process to consider only chemically viable compositions and structures. The challenge to realise this opportunity lies in developing methods to encode such qualitative knowledge into quantitative, algorithmically processable constraints without oversimplifying the problem and limiting the diversity of emerging solutions. An optimal solution may deviate from our expectations based on known chemistries.