Mathematically, given a (possibly incomplete) set of molecules m 1 , m 2 , . . . , m n ∈ M, and training data consisting of k pairs of examples with binary preference relations of the type m i ≻ m j (meaning that m i was preferred over m j in a specific match), our task is to infer a total ordering over all molecules in M. Furthermore, such pairs do not need to specify a complete ranking of the training data or be consistent (i.e., satisfy transitivity). In order to do so, we consider a function s : M → R, where we assume that each molecule can be parameterized by a latent score that can be learned by a sufficientlyexpressive model. Once this function has been approximated, it can be then used to impose a complete order over already seen or new molecules. Denoting by δ ij := ŝ(m i )ŝ(m j ) the learned latent score difference between molecules m i and m j , we estimate p (m i ≻ m j ) := σ (δ ij ), where σ is a sigmoid function.