The solubility of chemical compounds is a critical parameter in drug discovery, as it directly affects the bioavailability of the compound. Low solubility can result in inaccurate high-throughput screening (HTS) outcomes and can mask toxicity effects during early-stage drug development. The intrinsic solubility of a compound refers to its solubility in a neutral state at thermodynamic equilibrium between the solid and dissolved state. Although several modeling approaches have been developed to predict intrinsic solubility, usually small sets of compounds were used since acquiring such experimental data is time-consuming and costly, requiring the achievement of thermodynamic equilibrium and titration to obtain the pH at which the compound is neutral. A cheaper approach involves the determination of kinetic solubility, whereby compounds are first dissolved in DMSO and then added to water to determine precipitation concentration. The kinetic solubility of compounds is frequently higher than intrinsic solubility due to supersaturation and strongly assay dependent. An even faster approach, as used in this study, is to determine threshold solubility, which is crucial in assessing whether a given compound is soluble enough for an HTS screening assay. Although experimental methods can determine solubility, they are often time-consuming and require a considerable amount of resources. Furthermore, the number of compounds that need to be tested in the initial phase is substantial, making it challenging to determine the solubility experimentally. Therefore, approximations allow researchers to make a preliminary selection of promising compounds. Developing computational methods to predict solubility can increase the speed of approximations even further than the experimental approaches, and machine learning has emerged as a powerful tool to achieve this. While various computational methods for predicting solubility of compounds were developed, the conclusion from the recent challenge organized by JCIM was that prediction of this property remains a difficult and challenging task and no significant improvements in the field have been observed over the last ten years. In this study, we outline our success in the EU-OPENSCREEN -a not-for-profit European Research Infrastructure Consortium (ERIC) -and The Society for Laboratory Automation and Screening (SLAS) solubility challenge. The challenge was established to identify the state-of-the-art computational methods for reliable predictions of threshold solubility of compounds. Here, we present our consensus model which was selected as the winning solution and address challenges encountered during the challenge. Finally, we put our solution into a greater context and reconsider both our results as well as the data supplied by the challenge.