BO in molecular latent space requires inputs that are latent vectors z of a set of molecules after passing through the encoder of the VAE and their objective function values y to train the surrogate model f (z). Then, the acquisition function suggests new latent vector candidates ẑ that are decoded back to molecules so their objective function values ŷ can be calculated. These new data points are then used to update the surrogate model f (z) in the next iteration.