One can argue that the ultimate goal of machine learning in chemistry is to create a model that can generate molecules with a desired set of properties. This is also known as inverse design. Broadly speaking, there are two approaches. If we have large datasets, we can train generative models such as variational autoencoders (VAE) or generative adversarial neural networks (GANs). Without large datasets, evolutionary techniques such as genetic algorithms can generate novel, potentially interesting molecules . Those evolutionary methods work best if one can limit the underlying chemistry; for example, finding the optimal functional group on a material with a well-defined backbone. Given that the GPT-3 model can predict the properties of molecules and materials with a small data set, trying an inverse design strategy is tempting. This would be particularly important in the early stages of research; one often has a small set of experimental data points and a limited understanding. Yet, we could leverage a fine-tuned GPT-3 model to generate suggestions for novel materials with similar or even better performance. This would be an important step forward. Particularly since the tuning of such a natural language model is much more accessible than the training of conventional ML models. Here, we investigate this setting: Can a fine-tuned GPT-3 propose valid molecules that satisfy the constraints or desired properties specified in a prompt in natural language? Again, we are illustrating the potential for a few case studies.