Our function is the result of an interactive preference elicitation procedure. This approach necessarily involves a relatively low number of questions and answers compared to the number of data points typically used in machine learning approaches, which therefore does not allow us to determine uniquely the resulting function k. As a result, we do not claim that the resulting function k is the best possible to discover novel molecules: some arbitrariness is left with its parameterization. We do however stand behind the idea that this approach permits to understand the way the expert reasons and to justify the choice of the resulting class of functions. Going beyond this would require more parameters, in turn requiring a machine learning approach based on more data points than can be acquired with a human expert. We believe that combining such an approach with decision theoretic principles for guiding the choice of the functional form would be fruitful and leave this for future work.