The aim was to evaluate and review methods for prediction of the steady-state volume of distribution (V(D,ss)) of xenobiotics in man. For allometry, approximately 30-40% of predictions are classified as incorrect, humans and animals belong to different V(D,ss) categories for approximately 30% of the compounds, maximum prediction errors are large (>10-fold), the b-exponent ranges between -0.2 and 2.2 (averaging approximately 0.8-0.9), and >2-fold prediction errors are found for 35% of the substances. The performance is consistent with species differences of binding in and outside the vasculature. The largest errors could potentially lead to very poor prediction of exposure profile and failure in clinical studies. A re-evaluation of allometric scaling of unbound tissue volume of distribution demonstrates that this method is less accurate (27% of predictions >2-fold errors) than a previous evaluation demonstrated. By adding molecular descriptor information, predictions based on animal V(D,ss) data can be improved. Improved predictions (approximately 1/10 of allometric errors) can also be obtained by using the relationship between unbound fraction in plasma (f(u,pl)) and V(D,ss) for each substance (method suggested by the author). A physiologically-based 4-compartment model (plasma, red blood cells, interstitial fluid and cell volume) together with measured tissue-plasma partitioning coefficients in rats, f(u,pl), interstitial-plasma concentration ratio of albumin, organ weight and blood flow data has been successfully applied. Prediction errors for one basic and one neutral drug are only 3-5%. The data obtained with this comparably laboratory-intensive method are limited to these two compounds. A similar approach where predicted tissue partitioning is used, and a computational model, give prediction errors similar to that of allometry. Advantages with these are the suitability for screening and avoidance of animal experiments. The evaluated methods do not account for potential active transport and slow dissociation rates.