Chemical product design refers to the practice of developing novel chemical products given properties to be optimised and constraints to be satisfied. Strategies for chemical product design can be based on multi-objective constrained optimisation in a large search space of compounds whose properties are uncertain and partially known. Advances in machine learning, multi-objective optimisation, formal representation of chemical compounds and identified correlations between molecular structures and relevant properties, have fostered increased interest in computer-based techniques to identify candidate compounds for innovation in chemical products. In this paper we empirically explore a combination of state-of-the-art machine learning and evolutionary multi-objective optimisation methods to support chemical product design. In order to ground our arguments as concrete examples, we consider the design of domestic detergents, and explore how automating computational design can be controlled via specification of hyper-parameters, so as to generate solutions (detergents) with desired features. Our results contribute to the methodological problem of automating chemical product design, and more broadly functional molecular design.