How important is it to avoid A/B test collisions?

Let's say you have 3 A/B tests as such

  1. Control vs A
  2. Control vs B
  3. Control vs C

Unless you specifically work to ensure that tests do not collide (run on top of each other), you will end up with populations of users who see both A+B, B+C, A+B+C.

Technically, because if you're looking at results of experiment 1, both Control and A will see an equal number of pollution from 2 and 3, so the only difference in performance between 1. Control and A should be just the impacts of A.

Thoughts? Is it always best practice to avoid collisions or can we just assume, so long as pollution is evenly distributed, we're good?