One of the machine learning (ML) algorithms that successfully tackles this issue for molecular systems is called molecular representation learning (MRL), or pre-training, selfsupervised learning. In such algorithm, the property-predicting model is constructed with a pre-training learning process of tremendous unlabeled data, followed by the application of fine-tuning schema with labeled data on multiple downstream tasks. Proposed in 2022, Uni-Mol is an advanced MRL scheme that directly takes the 3D structures (instead of the 2D or 1D representation) of interested molecules as input, making it a competitive tool in predicting structural-related properties of various materials. In fact, by taking advantage of enormous 3D conformations of the target systems, Uni-Mol rivals the performances of the state-of-the-art (SOTA) methods in predicting energetic properties of open-source organic and medical molecules. In this work, we expand the application of Uni-Mol to the HTVS of organic luminescent molecules, of which the light-emitting properties are closely related to their 3D conformations. We demonstrate that with the accurately benchmarked QM calculations on a limited amount of systems combined with the Uni-Mol training process over millions of automatically constructed and semi-empirically optimized structures, photophysical properties of Ir(III) complexes, which are prototypical phosphorescent molecules, can be efficiently predicted, enabling the screen-out of presumably outstanding candidates. Most importantly, such computational protocol can be effortlessly transferred to other organic materials such as thermally activated delayed fluorescence (TADF) molecules and organic photovoltaic, enabling an efficient means of material design in a variety of fields.