The challenge of selecting the right protein foundation for enzyme engineering has long hindered progress in developing biological catalysts for medicine, manufacturing, and environmental applications. This bottleneck affects everything from drug synthesis to sustainable chemical production, where the wrong starting enzyme can derail months of optimization work. A new computational approach uses dual-encoder contrastive learning to rapidly identify the most promising enzyme candidates from nature's vast molecular library. The system analyzes protein sequences and structures simultaneously, learning to recognize patterns that predict catalytic potential for specific chemical transformations. By training on extensive databases of known enzyme-reaction pairs, the AI model can suggest optimal starting points for engineering campaigns with significantly higher success rates than traditional screening methods. This represents a fundamental shift from labor-intensive experimental screening toward predictive enzyme selection. The implications extend well beyond academic research into practical biotechnology applications. Pharmaceutical companies could accelerate drug manufacturing by quickly identifying enzymes for complex synthetic pathways. Environmental remediation projects could benefit from rapidly discovering proteins capable of breaking down specific pollutants. The approach also promises to unlock previously inaccessible chemical transformations by identifying unconventional enzyme families. However, the system's effectiveness depends heavily on training data quality and may struggle with entirely novel reaction types not represented in existing databases. While this computational advance won't eliminate the need for subsequent protein engineering, it could dramatically reduce the trial-and-error phase that currently dominates enzyme discovery, potentially cutting development timelines from years to months.