In this study, we introduce product-of-experts (PoE) chemical language models that efficiently navigate ultra-large virtual chemical spaces by combining a prior model pretrained on the target chemical space with expert and anti-expert models fine-tuned using external property-specific datasets (Figure ). To accurately assess the presence of generated compounds within the target chemical space, we constructed an ultra-large virtual chemical space containing approximately 10 billion compounds from predefined building blocks and chemical reactions. Using molecular docking against the dopamine receptor D2 (DRD2) as a case study, we demonstrate that the PoE chemical language model can generate compounds with desirable properties, such as improved docking scores and predicted blood-brain barrier (BBB) permeability. Notably, the majority of compounds generated by the PoE model were present within the target chemical space, indicating that these compounds are expected to be readily synthesizable using the available building blocks and chemical reactions. Our results suggest that the PoE chemical language model can serve as a scalable and flexible framework for navigating ultra-large virtual chemical spaces.