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We develop Latent Exploration Score (LES) to mitigate over-exploration in Latent Space Op- timization (LSO), a popular method for solv- ing black-box discrete optimization problems. LSO utilizes continuous optimization within the latent space of a Variational Autoencoder (VAE) and is known to be susceptible to over- exploration, which manifests in unrealistic solu- tions that reduce its practicality. LES leverages the trained decoder’s approximation of the data distribution, and can be employed with any VAE decoder–including pretrained ones–without addi- tional training, architectural changes or access to the training data. Our evaluation across five LSO benchmark tasks and twenty-two VAE mod- els demonstrates that LES always enhances the quality of the solutions while maintaining high objective values, leading to improvements over ex- isting solutions in most cases. We believe that new avenues to LSO will be opened by LES’ ability to identify out of distribution areas, differentiability, and computational tractability.