We assume a small initial dataset 𝐷 0 with molecules 𝑥 and their scores 𝑦 for the property of interest, either acquired beforehand from a chemist, or from experiments. In addition, there exists a pool of unlabeled molecules 𝒰, which can be shown to a chemist. The chemist's feedback 𝑦 is a score between [0,1] about the suitability of the molecule for the drug design task, with respect to the property of interest (0=not good, 1=very likely good). We assume a Gaussian likelihood of feedback: 𝑦 ∼ 𝑁(𝑓 * (𝑥), 𝜎 0 2 ), where 𝑓 * (𝑥) is the chemist's evaluation of the property of interest, and 𝜎 0 is the standard deviation of the noise in the chemist's answers. This means that the chemist's answers may be erroneous but are correct on average. For simplicity, we assume that the noise in the data generating process of 𝑦 in 𝐷 0 is the same as in the chemist's feedback. Molecules are represented by features, which in this work are descriptors such as physicochemical properties; 𝑥 ∈ ℝ 𝑝 , or Morgan fingerprints 𝑥 ∈ {0,1} 𝑑 , where 𝑑 is the dimensionality of the features.