Software and data availability for MLSB 2022 Submission. Protein language models (PLM) have recently been proposed to advance drug-target interaction (DTI) prediction, and have shown state-of-the-art performance on several standard benchmarks. However, a prevailing challenge for all DTI prediction models (including PLM-based ones), however, is distinguishing true drugs from highly-similar decoys. Leveraging techniques from self-supervised contrastive learning, we introduce a second-generation PLM-based DTI model trained on triplets of protein, drug, and decoys (small drug-like molecules that do not bind to the protein). We show that our approach, CON-Plex, improves specificity while maintaining high prediction accuracy and generalizability to new drug classes. CON-Plex maps proteins and drugs to a shared latent space which can be interpreted to identify mutually-compatible classes of proteins and drugs.