Binaural spatial audio reproduction systems use measured or simulated head-related transfer functions (HRTFs), which encode the effects of the outer ear and body on the incoming sound to recreate a realistic spatial auditory field around the listener. The sound localisation cues embedded in the HRTF are highly personal. Establishing perceptual similarity between different HRTFs in a reliable manner is challenging due to a combination of acoustic and non-acoustic aspects affecting our spatial auditory perception. To account for these factors, we propose an automated procedure to select the ’best’ non-individual HRTF dataset from a pool of measured ones. For a group of human participants with their own acoustically measured HRTFs, a multi-feature Bayesian auditory sound localisation model is used to predict individual localisation performance with the other HRTFs from within the group. Then, the model selection of the ’best’ and the ’worst’ non-individual HRTFs is evaluated via an actual localisation test and a subjective audio quality assessment in comparison with individual HRTFs. A successful model-aided objective selection of the ’best’ non-individual HRTF may provide relevant insights for effective and handy binaural spatial audio solutions in virtual/augmented reality (VR/AR) applications.