The rapid development of massive computational resources with advanced simulation and theoretical algorithms has made high-throughput virtual screening (HTVS) a groundbreaking tool in the design of new materials. By comprehensively exploring the chemical space with tailored properties, HTVS succeeds in predicting the most promising candidates for experimental validation, and the trial-and-error cost can be remarkably reduced. Recent applications of this approach include the search for both organic and inorganic materials in the field of batteries, 2D materials, alloys, semiconductors, catalyst, light-emitting devices, and photovoltaics, etc.. The central part of HTVS is the screening criteria, of which the generation relies on sufficient existing experimental data and/or accurate yet efficient quantum mechanical (QM) calculations. Unfortunately, experimental data under consistent conditions are rather limited, and highly accurate QM calculations are usually computationally demanding and even prohibitive to large realistic systems. Both of these experimental and theoretical obstacles pose practical challenges to the application of HTVS.