It was perfectly fine to work on any crazy ideas you wanted to. But if you wanted to publish as part of the CERN collaboration (and if you use CERN non-public data and CERN infrastructure, I'm not sure it was possible to publish as an "independent" anyway), there is a lot of protection to ensure that your article is at a good scientific standard.
As explained in the previous comment, scientists were nervous about "black box spurious pattern", "experimental artifacts" or "elsewhere effect". This was a good thing. The limitations were not there because of budget or access to infrastructure, but because people were conscientious of doing good science.
Remember the NN examples where the NN is given pictures of horses and pictures without horses and had to classify them, and it turned out one criteria was to detect the signature, in the bottom right, of the photographer that specializes in horse pictures? It meant that if you were giving a new picture of a bird but with that signature, the NN would say "it is a horse". Now, how do you make sure that something similar does not occur when treating billions of collision events? The categorized data, especially for the signal, is coming from simulation. What if your NN detects that in the signal, the pixel number 231 is not "hot" like the one in real data (because it turns out that, as sensor can be sometimes, this pixel is hotter in the real detector than it is in the simulation), and uses that to select the signal? Or that the NN overfits part of the data (the majority is classified on sensible cuts, but for more extreme cases, it classifies as signal events where there is an electron with pT between 24.5678 and 24.5685, but as background if it is before or after, because it happens to be what the training data looks like)?
Because of that, there were a lot of cross-checks and precautions being taken. One common approach was to compare more black-box-y methods with other methods that are more easily explainable. That was certainly a good thing. And this was not being seen as a chore: no one was approaching it as if people being good scientists where being personally against your new method. Proving that your new method was checking all the boxes of the scientific qualities was not seen as a bad thing, it was seen as the fun part of building new robust scientific advances.