  The No Free Lunch (NFL) theorem for search and optimisation states that
averaged across all possible objective functions on a fixed search space, all
search algorithms perform equally well. Several refined versions of the theorem
find a similar outcome when averaging across smaller sets of functions. This
paper argues that NFL results continue to be misunderstood by many researchers,
and addresses this issue in several ways. Existing arguments against real-world
implications of NFL results are collected and re-stated for accessibility, and
new ones are added. Specific misunderstandings extant in the literature are
identified, with speculation as to how they may have arisen. This paper
presents an argument against a common paraphrase of NFL findings -- that
algorithms must be specialised to problem domains in order to do well -- after
problematising the usually undefined term "domain". It provides novel concrete
counter-examples illustrating cases where NFL theorems do not apply. In
conclusion it offers a novel view of the real meaning of NFL, incorporating the
anthropic principle and justifying the position that in many common situations
researchers can ignore NFL.
