According to the VAE paradigm, any molecule corresponds not just to one vector, but to a domain in the embedding space R n . However, the regression function attributes different property values to each embedding, thus making the prediction ambiguous. We used the mean vector of normal distribution created by the VAE architecture as an input for the regression model. After the convergence in the latent space, the resulting vector does not a priori correspond to the mean of any molecule. A natural way to go around this problem is to decode the molecule from the found vector and then apply the encoder to calculate the mean embedding vector of the predicted molecule. Following this intuition, we suggest Algorithm B for molecule optimization. This approach allows us to look for an embedding that corresponds to a mean of some molecule and has the closest possible property value to the value of interest.