A recent editorial in Bioinformatics highlights the commonly misunderstood parameter of max_target_seqs in the BLAST alignment tool. The article raises the awareness of common misconceptions associated with a key parameter of BLAST. However, other key parameters, including the most commonly used evalue, have also been under scrutiny recently by the research community. A detailed documentation of these parameters remains lacking. Here we highlight and discuss the frequently misinterpreted feature of E-value specification in BLAST, and how this and other features may not be intuitive to users. These issues underlie the importance for all users to be critical and mindful of the implication of these parameters when interpreting the final search results. The NCBI BLAST alignment tool (Altschul et al., 1990) is one of the most commonly used bioinformatic tools to identify shared sequence similarity based on local alignment between a query sequence and a target sequence found in a database. Recently, Shah et al. (2018) highlighted a commonly misunderstood key parameter of this tool. Specifically, they highlighted the common misconceptions of the parameter –max_target_seqs, and the implications of its specification on the final results; issues related to this parameter, previously reported as ‘bugs’ in online posts (see https://gist.github.com/sujaikumar/504b3b7024eaf3a04ef5 and https://blastedbio.blogspot.com/2015/12/blast-max-target-sequences-bug.html), have not been widely appreciated in the research community. Another (and perhaps the most) frequently used option in BLAST is the filtering of results by the expect (E) value via parameter specification of –evalue. In BLAST, for each alignment between the query and a subject (target) sequence from a database, a bit-score (S) is calculated; an alignment and its S are determined by the specified parameters (e.g. word size, drop-off score for alignment extension, scoring/substitution matrix and gap costs) in the search (Altschul et al., 1990,, 1997; Camacho et al., 2009). The E-value for an alignment with a calculated bit-score X represents the number of alignments with S ≥ X that one would expect by chance in the search. A lower E-value indicates a smaller likelihood of observing the alignment (of score S ≥ X) simply by chance in the database, thus a higher ‘significance’ of the alignment (i.e. and of the match between the two sequences). The resulting E-value is dependent on the length of the query sequence, and the size of the database. According to the BLAST documentation (https://www.ncbi.nlm.nih.gov/books/NBK279690) and the frequently asked questions online (https://blast.ncbi.nlm.nih.gov/Blast.cgi? CMD=Web&PAGE_TYPE =BlastDocs&DOC_TYPE=FAQ), the E-value is meant to provide a ‘convenient way to create a significance threshold for reporting results’. Indeed, this option has been broadly used in numerous studies for almost three decades now to confine sequence-similarity search to a ‘significant’ threshold, and to identify significant similarity shared between two sequences. Many users assume that BLAST alignment hits with E-values less than or equal to the predefined threshold (e.g. 10−5 via the specification of –evalue 1e-5) are identified after the search is completed, in a final step to rank all alignments by E-value, from the smallest (on the top of the list of results) to the largest E-value (at the bottom of the list). However, the E-value filtering step does not occur at the final stage of BLAST; it occurs earlier during the scanning phase (Altschul et al., 1997; Camacho et al., 2009). During this phase, a gapped alignment is generated using less-sensitive heuristic parameters (Camacho et al., 2009); alignments with an E-value that satisfies the defined cut-off are included in the subsequent phase of the BLAST algorithm (and eventually reported). During the final (trace-back) phase, these gapped alignments are further adjusted using more-sensitive heuristic parameters (Camacho et al., 2009), and the E-value for each of these refined alignments is then recalculated. Therefore, non-intuitively perhaps, alignments that would have a final E-value lower than the specified threshold might not be reported among the final results, because they had failed (i.e. E-value larger than this same E-value threshold) at the earlier gapped-alignment stage. The issues related to parameter specifications (that affect how target sequences are reported in the final results) have been reported in a number of online forums. In addition to –evalue and –max_target_seqs, these parameters include –num_descriptions (the number of descriptions), and –num_alignments (the number of alignments) (see https://bioinformatics.stackexchange.com/questions/3409/blast-hits101disappearing-after-changing-evalue and https://bioinformatics.stackexchange.com/questions/2846/why-doesa-very-strong-blast-hit-get-lost-when-i-change-num-alignments-num-desc). These parameters when specified are not filters applied to the final results as one commonly assumes, but they influence the final results indirectly. A good understanding of how these parameters are implemented is important, especially in cases where the query sequences are highly divergent from other known sequences in the database (Rost, 1999). We concur with the call by Shah et al. (2018) to encourage all users to be critical and mindful of the implication of these parameter specifications on the final search results. Running BLAST searches following the ‘current standards’ (e.g. following the approach described in numerous research publications) without an adequate understanding of how each parameter works is not ideal. When in doubts, one may run the program at default settings and apply the filtering criteria directly on the results after the search is completed. We also recommend that the development team of BLAST to provide a more-explicit documentation on the implementation of these options in the algorithm, and how they would affect the final results. We thank Mark Ragan for his critical reading of this manuscript. RAGP is supported by an International Postgraduate Research Scholarship and a University of Queensland Centenary Scholarship. TGS is supported by an Australian Government Research Training Program (RTP) Scholarship. This work is supported by an Australian Research Council grant (DP150101875) awarded to Mark Ragan and C.X.C. Conflict of Interest: none declared.