The reprocessing of spent nuclear fuel is critical for the sustainability of the nuclear energy industry. However, several key separation processes present challenges in this regard, calling for continuous research into next-generation separation materials. Herein, we propose a high-throughput screening framework to improve efficiency in identifying potential ligands that selectively coordinate metal cations of interest in liquid wastes that considers multiple key chemical characteristics, including aqueous solubility, pKa, and coordination bond length. Machine-learning models were designed for the fast and accurate prediction of these characteristics by using graph convolution and transfer-learning techniques. Suitable ligands for Cs/Sr crystallizing separation were identified through the "computational funnel", and several top-ranking, nontoxic, low-cost ligands were selected for experimental verification.