The problem is that if our brains are not "magic" (essentially making this a religious argument), then they're operating via some algorithmic principles. If our brains are "algorithm generating algorithms", then I can in principle write an "algorithm generating algorithm" in silicon that does the same thing. And I know with mathematical certainty that my digital version is subject to NFL. So either my brain is too or we go back to religion to explain what non-physical process is responsible for our super-turing capabilities.
The second point is that NFL is often interpreted in a weird way, where we only think about "interesting problems". It is defined on the set of all search or optimization problems. It says if your algorithm is better than exhaustive search over some subset of problems, it must be worse on the complement of that set.
What does it mean for an algorithm to be efficient? Well, it's roughly speaking the number of steps it needed to take (assuming each step is the same amount of work, blah blah). OK, so an "efficient" algorithm must, by definition, prioritize some steps over others -- it's picking the "best" steps to take each time it has the choice. OK, so I'll just make up some instances of the problem that are custom-tailored so that what the algorithm thinks are the "best" steps always lead me in the wrong direction. You algorithm will then be worse than exhaustive search on my set of problems, precisely because it's choosing to avoid the steps I know to be good -- I defined the problem to make that happen.
That is true of any algorithm you can conceive of. There will be problems for which the bias that makes the algorithm good on the problems you intended it to work on will be exactly the wrong bias.
It doesn't matter if you say, "Aha, I'll let my algorithm generate new algorithms! Gotcha!" I'll just design a set of problems for which your algorithm generating algorithm will generate the wrong algorithms.
Search is always about bias. Without bias, you have random search -- that's literally the textbook machine learning definition of bias. All NFL says is that if you have to worry about every possible problem, any bias you choose will be worse than random sometimes. There's no escape clause here. Your algorithm generating algorithm is still a search algorithm with its own biases, and it will still be worse than random on some subset of all possible problems.