The use of AI in knowledge dense domains, e.g., chemistry, medicine, biology, etc. - is extremely promising, but often suffers from slow deployment and adaptation to different tasks. We propose a methodology to quickly capture the intent and expertise of a domain expert in order to train personalized AI models for specific tasks. Specifically we focus on the domain of polymer materials design and discovery: it often takes 10 years or more to design, synthesize, test, and introduce a new polymer material into the market. One way to accelerate up the design of polymer materials is through the use of computational methods to design the material, such as combinatorial screening, generative models, inverse design, etc. The drawback of these methods is that they generate a large number of candidates for new molecules, which then need to be manually reviewed by subject matter experts who select only a dozen for further investigation. Our solution is a human-in-the-loop methodology where we rank the candidates according to a utility function that is learned via the continued interaction with the subject matter experts, but which is also constrained by specific chemical knowledge. We prove the viability of our proposed methodology in a polymer production lab and we (i) evaluate against datasets of polymers previously produced in the lab as well as (ii) producing several novel materials that are undergoing experimental development, and (iii) quantitatively show that standard synthetic accessibility scores do not inform about patterns of SME decisions.