The reason you're running into multiple methods is because the target variability to visualize in a repeated measures design is not necessarily that straightforward to determine.
If you calculate the conventional SE then what you've done is give an estimate of how well you calculated the raw score. However, generally in a repeated measures design that wasn't the goal of the study. What you are typically looking to do is to calculate an effect. The variability of that effect is much less. I generally recommend plotting your effects only and the variability of your effect estimates (better as confidence intervals than SEs). Then the error bar will represent something about what you actually attempted to study. The effect SE will be the sqrt(MSe/n) where n is the number of measurements of the effect (not to be confused with number of S's).
When choosing a test you have to consider two important things:
A: is the test reliable when the ANOVA assumption have been violated;
the question is if the test performs well when the group sizes are different and when the population variances are very different or you have not normally distributed data;
B: does the test control over the Type I/Type II error rate; statistical power of a test and type I error rate are very related e.g: you can opt for a more conservative test, aiming small probability of Type I error, but you will loose statistical power. It is a trade-off.
Furthermore, Bonferroni and Tukey test are conservative - high control over type I eror rate bat low statistical power; Games-Howell is powerful but not appropriate for small sample. Games-Howell is accurate when sample sizes are unequal. For all of the you should be careful ANOVA assumptions;
Moreover you said: "My sample size is only 3 to 4 individuals per experimental group."
but I think this is not enough when it comes to test ANOVA assumptions.
This is a detailed book on the topic.
Andy Field is a great teacher and here has a nice video on post-hoc.
Also there and there are relevant documents on your question.
Regarding your question in comment:
I can say use this test or this one, but the main idea is that you have to know them well, the difference between them and the trade-off; after this you have to decide for one, two or more, and you have to be able to motivate and explain your decision and all of these in relation with your research and data not with the test 'per se'. Moreover, usually 'to assume' is not ok in statistics...therefore you have to test the normality and all ANOVA assumptions. Further, IMHO ANOVA it's ok but the group size is not ok. Considering your exigencies (in terms of significance level, power, no of groups etc.) you can compute a needed sample size per group ( using R, or using many other free resources on the web). I would like avoid to give you a 'cooked dish' because you wont gain anything, but to not make your life harder I say: if I were you I would use ANOVA, 30 individuals per group (for a 2X3 design you need n~180 individuals), I would use Tukey, REGWQ, and Bonfferoni.
Best Answer
My personal view on this is that
Finally, the following paper offers an interesting discussion of the use of error bars when presenting experimental results (and gauging significant difference from non overlapping error bars):