Should i drop simple features after deriving more complex features from them?
It will depend on the underlying relationship e.g.,
$Y = \alpha X_1 + \beta X_2 + \gamma X_1X_2$
In the above case, the Target is dependent on individual features as well as the Interaction features($X_1X_2$). So, you can't skip the individual.
Same will be True for polynomial too,
$Y = \alpha X + \beta X^2$
You may remove the original features for a scenario similar to this,
$Y = \alpha X_1X_2$ Or,
$Y = \alpha X^2$
Another intuition is that we never see a zero correlation to the original features nor we get a 100% correlation to the Interaction features.