Despite their significance in catalysis, medicine, and sustainability, transition metal complexes are challenging to model due to complicated electronic structures, limited experimental data, and a vast chemical space. This talk discusses recent advances in accurately predicting the structural properties of transition metal complexes with deep learning, facilitating the systematic exploration of chemical space. We conclude by demonstrating the integration of these models with generative AI for exploring novel organometallic chemistries and reactions.