This section introduces two HITL methods for setting objectives in de novo molecular design. The first is applicable when relevant sub-objective properties are known and available as scoring components: it adapts the MPO function (Section 2.2.1). The second is for cases where a specification of a scoring component for a molecular property does not exist, and we propose a method to learn a new predictive model that captures the medicinal chemist's knowledge about the molecular property (Section 2.2.2). In both cases, the AI-assistant needs to solve an active learning problem of how to select the molecules to show to the chemist during the interaction.