It is known that medicinal chemist's evaluation of molecules varies greatly between individuals and the answers are sometimes not consistent even for one person . Our probabilistic model tackles this using a noise model and by assuming that, on average, the answers are correct. This assumption may be sufficient in simple cases, and necessary for cases with little data. However, the chemist's feedback is likely to include biases, as they are ubiquitous in any human assessment. Ways to address the bias by soliciting answers from multiple experts have been studied in expert knowledge elicitation . Another possibility could be to learn about the biases of the experts, by using cognitive models and estimating their parameters to model the user behavior. To our knowledge this has not yet been done in HITL tasks.