where 𝒌 * is a vector with elements 𝑘(𝑥 * , 𝑥 𝑖 ), 𝑖 = 1, … , 𝑁 𝑡 , and 𝐾 𝑡 is a covariance matrix with entries 𝑘(𝑥, 𝑥') for each 𝑥, 𝑥' ∈ 𝐷 𝑡 . The vector 𝒚 contains all observations 𝑦 𝑖 , 𝑖 = 1, … , 𝑁 𝑡 . We apply two types of kernels: squared exponential for a case when 𝑥 are physicochemical properties, and Tanimoto kernel for Morgan fingerprint features. In our experiments, Morgan fingerprints resulted in better performance, and therefore, we focus on results with them. For completeness, the results with physicochemical properties are shown in the Supplementary material Section 3.2.