Over the past decade, machine learning (ML) has made significant inroads into various subfields of chemistry, facilitating the prediction of molecular properties such as solubility, bioactivity, toxicity, reactivity, as well as other physical properties at a much lower cost compared to traditional molecular modeling. Today, several readily available data-driven ML modeling platforms exist, including the open (quantitative) structureactivity/property relationship app (OPERA), or deep learning tools such as DeepChem, ChemML, ML4Chem, ChemProp, 28 DeepReac+, and OpenChem. These platforms have played a critical role in guiding research programs in drug discovery, chemistry, biology and material sciences. Despite the demonstrated remarkable performance in various tasks, these platforms often require large amounts of labeled data to achieve optimal performance, therefore limiting their use in cases where only compact datasets are available. To address this limitation, researchers have explored the use of chemically meaningful representations combined with the use of statistical or kernel-based models. This synergistic methodology has proven to yield reliable results while reducing computational cost and enhancing the model's interpretability compared to deep learning methods. This is particularly true for dealing with scarce and sparse chemical datasets commonly found in academic laboratories. The potential of such methods is exemplified by the development of platforms like Kraken, developed by Asparu-Guzik, Sigman, and co-workers, which employs regression models and pertinent molecular descriptors to create reliable models that facilitate the exploration of monodentate organophosphorus (III) ligands. Likewise, Milo et al. have highlighted the predictive and interpretable capabilities of linear regression models combined with statistical analysis and visualization techniques, advocating for user-friendly statistical interfaces to promote their integration into organic chemistry practices. In practice, the selection of an appropriate ML method depends on the specific scientific problem at hand and factors such as dataset size, feature complexity, and model interpretabil-ity. To effectively apply ML methods in chemistry, careful attention to three critical aspects is essential: dataset acquisition and curation, feature generation, and selection of ML methods. The quality of labeled datasets is crucial for generating accurate and predictive ML models. While large computationally generated datasets exist, experimental datasets remain essential as, in many cases, traditional molecular modeling approaches may not fully capture the complexity of experimental processes, resulting in unreliable estimates of observables. In addition, the bias toward publishing positive results can pose challenges in achieving balanced datasets and model performance. Secondly, identifying the features that better encode the required chemical information is not immediately evident. For example, while fingerprints are popularly used to generate predictive models, mainly due to their ease of implementation, they may not accurately capture the underlying chemistry, requiring the use of 3D descriptors in the model. Finally, the choice of the ML framework might not be evident a priori, and may require testing the performance of both classification (e.g., active/inactive) and regression (e.g., prediction of a continuous value) on the particular problem at hand. Striking a balance between accuracy and interpretability is vital, as the ultimate goal is to generate quantitative and interpretable ML models that facilitate the generation of new hypotheses and guide molecular design.