Transcriptome sequencing is now widely adopted as an efficient means to study the chemical diversity of venoms. To improve the efficiency of analysis of these large datasets, we have optimised an analysis pipeline for cone snail venom gland transcriptomes. The pipeline combines ConoSorter with sequence architecture-based elimination and similarity searching using BLAST to improve the accuracy of sequence identification and classification, while reducing requirements for manual intervention. As a proof-of-concept, we used this approach reanalysed three previously published cone snail transcriptomes from diverse dietary groups. Our pipeline method generated similar results to the published studies with significantly less manual intervention. We additionally found undiscovered sequences in the piscovorous Conus geographus and vermivorous Conus miles and identified sequences in incorrect superfamilies in the molluscivorus Conus marmoreus and C. geographus transcriptomes. Our results indicate that this method can improve toxin detection without extending analysis time. While this method was evaluated on cone snail transcriptomes it can be easily optimised to retrieve toxins from other venomous animals.