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Proteins play essential roles in cellular processes and are involved in numerous diseases, driving the need for efficient proteoform identification. Recent advances have brought nanopore-based protein fingerprinting within reach, potentially expanding the proteomics toolkit in the near future. Among emerging strategies, label-free, full-length analysis via free translocation is the most promising approach for detecting lowabundance proteoforms. While free translocation is typically accompanied by low temporal resolution, we demonstrate here that such measurements can enable reliable identification even for natural proteins with high sequence similarity. Combining low pH and guanidinium chloride, we generate a strong electroosmotic flow that enables efficient capture and translocation of unlabeled proteins with an aerolysin nanopore. Using machine learning classifiers, we achieve 80% accuracy in distinguishing seven related proteins, based on distinct and directional fingerprints with high reproducibility. Differences in fingerprints partially reflect the distribution of volume and charges in the protein sequences and may contain additional contributions from the translocation dynamics. Our findings position free-translocation measurements of unfolded proteins as a promising approach to fingerprinting without the need for chemical conjugations or enzymatic digestions. With further development, fingerprint-predictions could allow to infer de novo protein sequence information from single-molecule data, offering a powerful tool for proteomics.