In a prospective, randomized medical study, patients enroll to the trial in a non-systematic fashion. The panel of eligible participants is actually a moving window of time in which subjects are convenience sampled: an offer to enter the trial is made to the first people who stroll up until the trial is fully enrolled. It's well known that this sample is not at all representative of the total patient population - and that's hardly the chief concern of medical research today! This drift exacerbates for each conditional step of the study: performing a screening visit, signing informed consent, and - for non-randomized set analyses - complying with study procedures.
But simple random sampling is just not possible in research. Consider cancer patients. At this moment, say there are 1 million people with a subtype of relapsed/refractory multiple myeloma in the world. I cannot cold-call every single number to identify them, and even if I did, the timeline for my drug approval is 5-10 years after which nearly every living person with this disease will die - the point of my drug is to treat the next 20-40 years of patients with this disease on the assumption that something even better will come along at that point. "Time stops for no one."
We cannot assume that the time-series of patients in the sampling window are random or independent. As I mentioned, all individuals with a condition will be invited to participate, and they are screened and enrolled on a first-come-first-serve basis until target recruitment is achieved. The biases of a convenience sample are prevalent case bias and lead time bias. Both of these scenarios are instances where subjects merely show up more often to the hospital/clinic where recruitment is performed because a. they already had the condition and were receiving care for it, or b. they are more cautious and show up more often.
Randomization does not eliminate these biases. Randomization can balance covariates so there is no confounding of intent-to-treat analyses. However, the actual treatment effect may remain unknown. Even so the study may be well motivated, we chose time invariant analyses and assess the assumption of undetected interactions so that, even if the estimate is biased, there's little if any chance that the effect is actually reversed. In this case, the inference on the central hypothesis of "no effect" is well conserved. For example, a drug may confer a 0.50 risk reduction for an adverse outcome in the population, but because of healthy participant bias, the trial estimates a 0.75 risk reduction, however the upper bound of the 97.5% CI does not include 1 so we can conclude that the drug is beneficial at the one-sided 0.025 alpha level.
So this example contributes to a "no" response to the question. When SRS can be done, it is preferred in all respects. But cost, time, and other considerations don't always permit. In fact, complex sampling procedures can only be said to make the sample less random yet more efficient. This extends all the way to the above example where the sampling is entirely deterministic.