dataset is generated, its further usage in applications such as machine learning tends to be limited due to the inherent imbalance of HTS screens, with inactive samples far outnumbering active ones. This stark imbalance biases machine learning algorithms to the inactive class, reducing their potential to identify further active compounds via machine learning. To avoid this issue, identifying the most important samples in an HTS dataset could be used to undersample and balance the datasets. To tackle all these limitations in HTS pipelines, we introduce a variety of machine learning data valuation approaches to the field of drug development pipelines. Data valuation methods estimate each training instance's impact on the optimum found by a machine learning or deep learning model. Some samples carry high amounts of information helpful for a machine learning model, some samples carry very little useful information, and some samples actually carry harmful information that actively worsens the models performance. Data valuation methods can identify these importance scores using various machine learning models and data valuation concepts. Some methods calculate the importance of a training sample on the prediction of a separate test set, while other methods score a training sample's importance on its own prediction. These compound-specific importance scores can be utilized to guide screening efforts, identify false positives, and remove uninformative compounds, tackling significant hurdles in current HTS pipelines. Within this study, we benchmarked gradient boosting-based influence functions such as MVS-A and CatBoost's object importance, the deep neural network-based approach TracIn, as well as data valuation using reinforcement learning (DVRL), and a KNN approximation of the cooperative game theory concept of Shapley values. To evaluate these data valuation approaches for the application of active learning and false positive detection, we curated a selection of 25 HTS datasets with different sizes, class imbalance, biological targets, assay technology, and false positive rates based on the Multifidelity PubChem Bioassay (MF-PCBA) benchmark. Additionally, to analyze the data valuation approach for undersampling, the MolData repository was utilized. Our results highlight the potency of data valuation methods to identify informative compounds in HTS applications effectively. The implications of these developments are far-reaching, offering the potential to expedite the drug discovery pipeline and to make substantial contributions to pharmaceutical research. This study provides a critical step forward in integrating machine learning with HTS, potentially transforming the drug development landscape. Therefore, to allow the scientific community to apply and improve upon the methods and applications used in this study, we created a benchmarking platform that allows straightforward application and modification of the data valuation methods to a wide range of datasets. The platform provides a modular toolbox for dataset loading and processing, allowing easy integration of your own data, as well as the base data valuation methods and applications used herein.