Plasmids play an important role in bacterial evolution and mediate horizontal transfer of genes including virulence and antimicrobial resistance genes. Although short-read sequencing technologies have enabled large-scale bacterial genomics, the resulting draft genome assemblies are often fragmented into hundreds of discrete contigs. Several tools and approaches have been developed to identify plasmid sequences in such assemblies, but require trade-off between sensitivity and specificity. Here we propose using the Kraken classifier, together with a custom Kraken database comprising known chromosomal and plasmid sequences of Klebsiella pneumoniae species complex (KpSC), to identify plasmid-derived contigs in draft assemblies. We assessed performance using Illumina-based draft genome assemblies for 82 KpSC isolates, for which complete genomes were available to supply ground truth. When benchmarked against five other classifiers (Centrifuge, RFPlasmid, mlplasmids, PlaScope and Platon), Kraken showed balanced performance in terms of overall sensitivity and specificity (90.8 and 99.4 %, respectively, for contig count; 96.5 and >99.9 %, respectively, for cumulative contig length), and the highest accuracy (96.8% vs 91.8-96.6% for contig count; 99.8% vs 99.0-99.7 % for cumulative contig length), and F1-score (94.5 % vs 84.5-94.1 %, for contig count; 98.0 % vs 88.9-96.7 % for cumulative contig length). Kraken also achieved consistent performance across our genome collection. Furthermore, we demonstrate that expanding the Kraken database with additional known chromosomal and plasmid sequences can further improve classification performance. Although we have focused here on the KpSC, this methodology could easily be applied to other species with a sufficient number of completed genomes.