(10) where f ⋆ (x) ∈ R corresponds to the penalized LogP score oracle. This model assumes that, on average, an expert would accurately tell if a given query molecule x t is likely to exhibit moderate lipophilicity up to some normally-distributed noise. For each x t , a noise value is sampled from a Gaussian distribution with mean 0 and standard deviation σ ϵ to mimic cases where expert evaluation may deviate from that of the ground truth. We consider σ ϵ = {0.5, 1.0, 2.0} to evaluate our approach's sensitivity to varying levels of noise in expert feedback. We also consider the ideal scenario where queries are directly evaluated by the oracle (i.e., ϵ = 0).