The human ether-a-go-go-related gene (hERG) encodes for the poreforming alpha-subunit of voltage-gated potassium ion channels. The hERG channel regulates the efflux of potassium ions in cardiac myocytes and thereby plays a key role in coordination of heartbeat. Literature indicates that blockade of hERG channel leads to prolonged QT interval of the action potential which can result in fatal cardiac arrhythmia (Torsade de pointes). Several marketed drugs, including antiarrhythmic agents, were withdrawn after being reported to trigger cardiac arrhythmia that sometimes led to sudden death. Consequently, hERG channel emerged as an important off-target, marking early assessment of hERG liability an essential step in drug discovery. The gold-standard in vitro and in vivo assays that facilitate screening of hERG channel inhibition are expensive and provide low throughput. Meanwhile, in silico methods emerged as an alternative for early assessment of pharmacological and toxicological effects of chemical substances. Multiple studies reported in silico models for predicting hERG channel inhibition over the past several years. Tropsha et al. provided an overview of quantitative structureactivity relationship (QSAR) studies from the literature that were reported before 2014. More recently, several other studies reported models based on simple methods like read across and machine learning (ML) methods , including deep neural networks (DNNs). Many models are based on proprietary or in-house datasets which restricts the use of this data to build newer models for academic drug discovery. It is suspected that hERG channel can bind a wide variety of chemotypes and a major limitation to developing robust prediction models using publicly-domain hERG activity data is the fairly limited chemical diversity of available training data. Although different combinations of ML algorithms and molecular descriptors have been tested, there is no combination of choice that performs well on unseen data. For instance, a consensus of Support Vector Machines, Random Forests, Gradient Boosting Model and Tree Bagging provided better performance in comparison to individual models that performed very similar to each other when used with different descriptors. Recent studies suggest that neural networks based on learnable representations offer broadly a better performance than classical ML algorithms. Latent descriptors that are derived directly from the neural network architecture are gaining popularity in molecular property prediction. Furthermore, descriptor-free QSAR models that are based on recurrent neural networks (RNNs) and molecular representations like SMILES were reported to demonstrate superior generalization capabilities on out-of-domain test data. Despite this body of literature, no one has attempted to compare these methods against each other in a prospective validation study. In this study, we enriched the public domain hERG data with a dataset comprising bioactive compounds screened in a homogeneous high-throughput assay of hERG channel inhibition to provide the community with a large reference dataset of high integrity. The primary goals of the study are to develop prediction models representing both classical and novel AI developments, validate the best models on prospectively screened compounds, as well as on recently approved FDA drugs with hERG liability data.