The growing scale and number of biometric deployments around the world necessitates research into technologies which facilitate fast identification queries and high discriminative power. In this context, this article presents a biometric identification system which relies on a successive pre-filtering of the potential candidate list using multiple biometric modalities, coupled with a weighted score-level information fusion. The proposed system is evaluated in a series of experiments using a compound dataset constructed from several publicly available datasets; an open-set identification scenario is considered with the enrolment database containing 1,000 chimeric instances. This evaluation shows that the proposed system exhibits a significantly increased biometric performance w.r.t. a weighted score-level or rank-level fusion based baseline, while simultaneously providing a consequential computational workload reduction in terms of penetration rate. Lastly, it is worth noting that the proposed system could be flexibly employed in any multi-biometric identification system, irrespective of the chosen types of biometric characteristics and the encoding of their extracted features.