The efficient sampling of the latent space of the generative model is the key to the design of new cage molecules with a desired property. Here, we would like to uncover new cage molecules that are shape-persistent. The well-trained gradient of the probability of shape persistence formed in the latent space enables the use of multiple gradient-based optimisation methods, such as Bayesian optimisation (BO) and reinforcement learning, that result in the conditional generation of non-collapsed cages. We use two different strategies, BO and interpolation, to demonstrate that our model is capable of combining different efficient sampling methods to realise the generation of new cage molecules.