Researchers frequently depend on unexpected results, which can delay progress and make it challenging to achieve specific design goals. In contrast, generative models implement systematic strategies for molecule design by implicitly recognizing and leveraging complex patterns within existing data. Through deep learning, the complex feature representations related to targeted properties can be identified and extracted, which, however, are impossible to accomplish simply via chemical intuition and knowledge using traditional strategies. These implicit features can be parametrized via neural networks for recurrently directed search in chemical space.