CoPriNet alleviates this problem, as it relies on a deep learning model trained to predict compound prices using only their molecular 2D graph. Our approach not only exhibits far better performance than existing alternatives, as evaluated on an independent test set, but also has excellent throughput, being able to process up to one thousand molecules per second. This speed means that CoPriNet can be deployed for high-throughput problems such as virtual screening or de novo compound generation/optimization, where more complex retrosynthesis-based approaches are too computationally demanding.