Utilizing the calculated means and covariance matrices, we developed a function to draw samples that conform to the observed empirical distributions of molecular properties. For each set of conditions-defined by a specific bin index and property column-this function generates samples from a multivariate normal distribution. The parameters of this distribution are directly derived from the mean and covariance statistics of the bin, ensuring that each sample reflects feasible combinations of molecular properties. This method significantly reduces the likelihood of proposing chemically implausible or unrealistic molecules as inputs to the generative model.