The idea about interactive multi-parameter optimization of molecules is starting to raise interest, but to date few works exist and to our knowledge none of them show a proof of concept that interaction with a human chemist actually helps optimization, which is the key contribution in this work. A web application gruenif.ai optimizes molecules in a continuous vector space, starting from an input molecule, and allows a user to observe intermediate result molecules and give feedback (good/not good) to them. Difference to our approach is that the method does not actively select the molecules that are shown. Furthermore, the user feedback is not used to adapt the MPO objective directly, instead, the objective must be defined by the user and the feedback enters as a separate chemical desirability score. This work divides the problem of interactive adaptation of the MPO objective function into two tasks that are implemented independently: In Task 1, depicted in Figure , the high-level goal is to infer the parameters of the desirability function of each property in a MPO function: A chemist inputs a set of molecular properties they wish to optimize, and their weights. What is unknown is which values of the properties are good, i.e. the desirability functions. An initial guess about the good interval of each property is given by the chemist, but they are refined by the algorithm based on the chemist's feedback. In Task 2, the goal is to build a chemist-specific scoring component for a molecular property for single parameter optimization; the same component can later act as part of the objective in an MPO. The chemist evaluates the score of molecules with respect to the property to be optimized. This feedback is used to learn a non-parametric model, which can be used during molecular optimization to generalize the chemist's feedback to new molecules.