Estimating binding energies is vital for drug discovery, yet supervised methods are hampered by limited experimental data. Recent protein structure predictors (e.g. AlphaFold3) offer unsupervised alternatives via confidence metrics that correlate with binding energies. However, these metrics operate on a fixed scale, limiting their ability to capture fine-grained energy differences. Leveraging the Joint Energy-based Model (JEM) framework, we show that protein structure predictors implicitly define an energy function, and we introduce three new energy-based models derived from the distogram and confidence heads. Our EBMs consistently improve binding energy prediction, outperforming both traditional confidence metrics and unsupervised baselines, and demonstrate that structure prediction models can be repurposed as powerful unsupervised energy predictors.