A broad range of computational models is available for animal-free chemical safety assessment. The models are used to predict a variety of endpoints, including adverse effects or apical endpoints, toxicokinetic properties, and exposure, often from chemical structure or in vitro inputs alone. To support their wider use, such models need to be findable, accessible, interoperable, and reusable (FAIR). This study has reevaluated the existing FAIR principles applied to quantitative structure-activity relationships (QSARs) in order to adapt these principles to a wider range of computational models. Despite the breadth and variety of approaches, many computational models comprise common components including the training series, information about the modelling engine, and the model itself. As a result, a refined set of four FAIR Lite principles is proposed based on the methodological foundations of computational toxicology which are unambiguously understood by practitioners such as developers and end-users. To this end, it is proposed that to comply with the original FAIR principles, a computational toxicology model should be associated with (i) a globally unique identifier for model citation; (ii) the capture and curation of the model; (iii) the metadata for the dependent and independent variables and, where possible, data; and (iv) storage in a searchable and interoperable platform. The FAIR Lite principles are mapped onto the original FAIR principles applied to QSARs, thereby demonstrating that a simpler checklist approach covers all aspects. Plain language summary Many types of computational models are used in animal-free chemical safety assessment. These are used to make predictions for numerous endpoints, primarily focusing on the hazardous properties of, or exposure to, a chemical substance. The models use information from chemical structures and/or properties, or other non-animal data as inputs. It is essential that the risk assessor or toxicologist can find and utilize the models with confidence. The previously developed findable, accessible, interoperable and reusable (FAIR) principles for computational models are a framework intended to ensure that models are accessible and stored appropriately. This investigation has refined the original FAIR principles applied to computational models to capture information for all types of modelling approaches that may be used in chemical safety assessment. The new principles, termed FAIR Lite, encapsulate the original principles in four criteria relating to identifiers, description of a model, its (meta)data, and storage