Decoys were generated through the application of DeepCoy, were strategically selected to align with the chemical properties of active molecules, while a mismatch in chemical structure was concurrently ensured to minimize the potential for false negative bias. DeepCoy employed an extensive array of over 25 physicochemical properties, including molecular weight, the number of rotating bonds, the total number of hydrogen donor and acceptor groups, logP, polar surface area, and the sp3 fraction of carbon atoms, among others. This comprehensive approach, coupled with a high frequency of repetitions, facilitated the creation of decoys that were challenging to differentiate from active molecules. A critical aspect of the DeepCoy model was its ability to mitigate three prevalent biases in virtual screening datasets: artificial enrichment, analogue bias, and false negative bias. This highlights the significant impact of advanced modeling techniques in enhancing the accuracy and reliability of virtual screening processes.